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Results for “Predictive modeling”

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At least 361 records · Page 20Linked to original sources

PubMed1997

Predictive model to identify trauma patients with blood alcohol concentrations > or = 50 mg/dl.

OBJECTIVE: To develop a simple model for identification of trauma patients who are likely to have a blood alcohol concentration > or = 50 mg/dL (BAC + 50). METHODS: Demographic, clinical, and BAC data were collected from the clinical trauma registry and toxicology data base at a Level I trauma center. Logistic regression was used to analyze data from 11,206 patients to develop a predictive model, which was validated using a subsequent cohort of 3,523 patients. RESULTS: In the model development cohort, alcohol was detected in the blood of 3,180 BAC-tested patients (28.7%), of whom 91.2% had a BAC + 50 status. Preliminary analysis revealed associations between a BAC + 50 status and sex, age, race, injury type (intentional vs. unintentional), and time of injury (night vs. day and weekend vs. weekday). A predictive model using four attributes (sex and injury type) identified patients at low, medium, and high risk for being BAC + 50. The model was validated using the second group of patients. CONCLUSION: Injured patients with a high probability of being alcohol positive can be identified using a simple scoring system based on readily available demographic and clinical information.

Adolescent↗

PubMed2003

Validation and calibration of the Kabi Pharmacia International Growth Study prediction model for children with idiopathic growth hormone deficiency.

In 1999 a model was published for prediction of growth in children with idiopathic GH deficiency (IGHD) during GH therapy, derived using data from the Kabi Pharmacia International Growth Study (KIGS) database (Pharmacia \|[amp ]\| Upjohn, Inc., International Growth Database). We validated and calibrated this KIGS model for growth in the first year of GH therapy using data from 136 Dutch children with IGHD. Observed vs. predicted outcomes were plotted, and the fitted regression line was significantly different from the line of identity (P = 0.03). It appeared that the predictions were too extreme: relatively low predictions were too low, relatively high predictions were too high. This is a well known phenomenon in the context of prediction models, called overoptimism. For valid application to other data the KIGS predictions should be calibrated. Calibrated predictions are obtained using Y(cal) = Y(orig) + (2.153 - 0.192 x Y(orig)), where Y(cal) is the calibrated prediction, and Y(orig) is the KIGS prediction. The calibrated prediction will be higher than the original KIGS prediction when the original prediction is less than 11.2 cm/yr and will be lower otherwise. The variability of the prediction errors of the calibrated predictions was positively related to the value of the prediction (P < 0.001), described by the equation SD(pred err) = -1.017 + 0.286 x Y(cal). Our calibrated model will give better predictions for children with IGHD fulfilling the same criteria.

Calibration↗

PubMedDate not supplied

In vitro phototoxicity testing: development and validation of a new concentration response analysis software and biostatistical analyses related to the use of various prediction models.

As demonstrated in several validation studies, the dermal phototoxic potential of chemicals in humans can be effectively assessed by in vitro methods. The core of these methods is to monitor dose-response curves of a chemical in the absence and presence of light, to quantify the difference between these two curves by appropriate measures (either the photo-irritancy factor [PIF], or the mean photo effect [MPE]), and to use these measures as predictors of in vivo phototoxicity. We present new concentration-response analysis software for in vitro phototoxicity testing, which runs on current personal computers, and takes into account all the limitations identified when using a former program. We also demonstrate the validity and robustness of this new software by applying it retrospectively to all data available from two phases of the EU/COLIPA validation trial for the 3T3 neutral red update in vitro phototoxicity test. Some frequently raised questions pertaining to the use of prediction models in phototoxicity testing are addressed, including: the necessity of using prediction models based on a cut-off; whether it is justifiable to use sharp prediction cut-off values; whether there is a biostatistical justification for the highest concentration of the test chemical; and whether repeated testing of a chemical is required.

3T3 Cells↗

PubMed2001

Predictive modeling care management program produces dramatic bottom line results.

Predictive modeling program identifies high-risk patients before complications lead to skyrocketing costs. It's a new-generation effort, but it has already proven its worth in a large population, producing savings of $1 million to $2 million per month. The effort begins with the development of a constantly evolving registry of high-risk patients. Specialized case management efforts are then focused on stabilizing individuals within this group. See how traditional interventions have been combined with new technology to deliver results.

Algorithms↗

PubMed2004

Principal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples.

Principal component analysis (PCA) was used to identify the main sources of variation in the Fourier transform infrared (FT-IR) spectra of 329 wines of various styles. The FT-IR spectra were gathered using a specialized WineScan instrument. The main sources of variation included the reducing sugar and alcohol content of the samples, as well as the stage of fermentation and the maturation period of the wines. The implications of the variation between the different wine styles for the design of calibration models with accurate predictive abilities were investigated using glycerol calibration in wine as a model system. PCA enabled the identification and interpretation of samples that were poorly predicted by the calibration models, as well as the detection of individual samples in the sample set that had atypical spectra (i.e., outlier samples). The Soft Independent Modeling of Class Analogy (SIMCA) approach was used to establish a model for the classification of the outlier samples. A glycerol calibration for wine was developed (reducing sugar content < 30 g/L, alcohol > 8% v/v) with satisfactory predictive ability (SEP = 0.40 g/L). The RPD value (ratio of the standard deviation of the data to the standard error of prediction) was 5.6, indicating that the calibration is suitable for quantification purposes. A calibration for glycerol in special late harvest and noble late harvest wines (RS 31-147 g/L, alcohol > 11.6% v/v) with a prediction error SECV = 0.65 g/L, was also established. This study yielded an analytical strategy that combined the careful design of calibration sets with measures that facilitated the early detection and interpretation of poorly predicted samples and outlier samples in a sample set. The strategy provided a powerful means of quality control, which is necessary for the generation of accurate prediction data and therefore for the successful implementation of FT-IR in the routine analytical laboratory.

Calibration↗

PubMed2003

Quantitative predictive models for octanol-air partition coefficients of polybrominated diphenyl ethers at different temperatures.

Quantitative predictive models for octanol-air partition coefficients of polybrominated diphenyl ethers at different environmental temperatures (T) were developed. Partial least squares (PLS) regression was used for model development. A list of 18 theoretical molecular structural descriptors was screened by PLS analysis. The optimal model was selected from the one containing nine theoretical molecular descriptors and 1/T as predictor variables. The cross-validated Q(2)(cum) value for the optimal model is 0.975, indicating a good predictive ability and stability of the model. Intermolecular dispersive interactions play a leading role in governing the magnitude of logK(OA). The lower the E(LUMO) (the energy of the lowest unoccupied molecular orbital), the greater the intermolecular interactions between octanol and PCB molecules, and thus the greater the logK(OA) values.

Environmental Pollutants↗

PubMed1996

Evaluation of the white finger risk prediction model in ISO 5349 suggests need for prospective studies.

The risk prediction model for white fingers in Annex A of ISO 5349 is not likely to offer protection from all tools and all work processes. It is also probable that some work place changes it has initiated are either redundant or lack the intended effect. The main reasons for these shortcomings are the following. The often demonstrated disagreement between predicted and observed white fingers occurrence may be related to the fact that the model is based on latency data. This leads to an overestimation, to an unknown extent, of true group risks. A possible healthy worker effect, resulting in underestimation, has not been considered, and uncertainty because of recall bias is connected with using latency as effect variable in a slowly developing disorder like white fingers. The diagnostic criteria for white fingers have varied over the years, causing a possible inclusion of circulatory disturbances other than those induced by vibration. Among insufficiently clarified matters unrelated to vibration are variations in individual susceptibility and other host factors that modify vibration effects, uncertainty concerning daily or total effective exposure, and the fact that variation in work methods and processes as well as ergonomic factors other than vibration tend to make different groups incomparable form the viewpoint of risk of injury. Lack of sufficient data on vibration measurements and employment durations add to the uncertainty, as do variations in tool conditions (grinder wheels, etc) and inherent difficulties in measurement. Finally, the ISO 5349 frequency-weighting curve only relates to acute sensory effects rather than chronic effects on vascular functions like white fingers, and directional difference in sensitivity has not been incorporated in the curve. Data on exposure-response relationships are needed from prospective studies that monitor the dose of exposure to special vibration types and all relevant environmental agents, employ diagnostics with good sensitivity, specificity and predictive value, and pay attention to environmental or individual confounding factors and effect modifiers. Before such data are available, the ISO 5349 model should not be used for risk prediction. It can serve, however, as an incentive for manufacturers to produce tools that vibrate less, and for employers to implement practical measures in order to reduce the total and dose of effective exposure.

Fingers↗

PubMed1997

Analyzing predictive models following definitive radiotherapy for prostate carcinoma.

BACKGROUND: As we approach the 21st century, clinically useful predictive models for prostate carcinoma are urgently needed to stratify patients reliably for future treatment strategies. Recently, many investigators have developed models that employ prostate specific antigen (PSA)-based constructs or groupings in an attempt to predict outcome accurately following definitive radiotherapy. This investigation was conducted to determine which of these models provides the closest "fit" to independent clinical outcome data measuring biochemical freedom from failure (bNED control), thereby warranting further exploration. METHODS: Six models were analyzed in a definitive radiotherapy series of 421 patients with localized prostate carcinoma treated with a median dose of 74 Gray (Gy) between March 1988 and November 1994. A stepwise Cox proportional hazards multivariate analysis (MVA) was performed to predict for bNED control using the following covariates: PSA, Gleason's score, stage, dose, PSA density, and perineural invasion. Subsequent MVAs were performed for each model incorporating the new construct or prognostic groupings. The adequacy of the models was confirmed using plots of score residuals against time to bNED failure and comparisons were made used Akaike's Information Criteria (AIC) in which a smaller value corresponds to a statistically improved model based on explained variation and the number of predictors. Because PSA was distributed in a log-normal fashion in the current study population, the model-building process was duplicated using a logarithmic transformation analysis. Biochemical failure was defined as 2 consecutive elevations in the PSA > or = 1.5 ng/mL. The median follow-up time was 34 months (range, 2-87 months). RESULTS: Initially, the model developed by Pisansky et al. appeared the most predictive due to the parsimony in their risk estimate, which is the sole predictor of outcome, as well as its associated lowest AIC value. However, after the logarithmic transformation analysis, all the models appeared to be equally predictive of bNED outcome. CONCLUSIONS: A plethora of accurate models for predicting outcome following definitive radiotherapy for prostate carcinoma recently have been engineered, all of which are essentially equally predictive in this data base (via a logarithmic conversion process). This analysis should be corroborated in other large radiotherapy series.

Follow-Up Studies↗

PubMed2005

A predictive model for exemestane pharmacokinetics/pharmacodynamics incorporating the effect of food and formulation.

AIMS: Exemestane (Aromasin) is an irreversible aromatase inactivator used for the treatment of postmenopausal women with advanced breast cancer. The objective of this study was to evaluate the effect of formulation comparing a sugar-coated tablet (SCT) with a suspension and food on the pharmacokinetics (PK) and pharmacodynamics (PD) with respect to plasma estrone sulphate (E1S) concentrations of exemestane, using a PK/PD approach. METHODS: This was an open, three-period, randomized, crossover study. Twelve healthy postmenopausal women received single oral doses of 25 mg exemestane as a SCT after fasting or food and as a suspension after fasting. Exemestane and E1S concentrations were determined before and up to 14 days after drug administration. Population analysis was performed in two steps: (i) a compartmental PK model was selected incorporating the effect of food and formulation; (ii) conditional on the PK model, a PD model was developed employing indirect response models. Model selection was performed using standard statistical tests. Validation and assessment of the predictive capability of the selected model was performed using real test data sets obtained from the literature. RESULTS: A three-compartment model with first-order elimination rate best described exemestane disposition (k12 0.454, k21 0.158, k13 0.174, k31 0.016 and k 0.738 h(-1)). Absorption was described by a mono-exponential function [ka 2.3 (SCT after fasting), 1.1 (SCT after food) and 7.6 h(-1) (suspension); lag time 0.2 h]. The PD model assumed that E1S plasma concentrations are determined by a zero-order synthesis rate (6.5 pg ml(-1) h(-1)) and a first-order elimination constant (0.032 h(-1)). Exemestane inhibited E1S synthesis with a C50 value of 22.1 pg ml(-1). The mean population estimates were used to simulate the administration of different doses of the drug (0.5, 1, 2.5, 5 and 25 mg day(-1)). The model predictions were in agreement with historical data. CONCLUSIONS: Exemestane absorption is influenced by the formulation of the drug and by food, but its disposition is independent of both. PK differences do no translate into clinically important differences in the PD. The PK/PD model developed was able to predict successfully the response to different doses and administration schedules with respect to oestrogen suppression.

Administration, Oral↗

PubMed1994

Development of a predictive model for optimal zona pellucida binding using insemination volume and sperm concentration.

OBJECTIVE: To develop a predictive model under hemizona assay (HZA) conditions for human spermatozoa concentrations and insemination volume for optimum zona pellucida (ZP) binding. DESIGN: Analysis of 20 different insemination volumes for zona binding and sperm morphology under HZA conditions. SETTING: Reproductive biology unit, tertiary medical center. PATIENTS: Four proven fertile sperm donors. MAIN OUTCOME MEASURES: 5-, 20-, 50-, 80-, and 100-microL droplets were analyzed with four different concentrations of 0.5 x 10(6), 1.0 x 10(6), 2.0 x 10(6), and 4.0 x 10(6) cells/mL to determine the number of sperm bound to each hemizona. Fifteen hemizonae were used for each insemination volume or microdroplet. Response surface regression model with volume and concentration as the regressor variables has been used. RESULTS: The response surface of binding for the factors concentration and volume showed nonlinear association. A formula, indicating the optimal sperm insemination volume for maximum sperm binding to the ZP, Vmax = -(b1 + b5c)/2b6c, is described. The transformed data indicated 60 microL containing 4 x 10(6) sperm/mL to be optimal. Although morphology of zona spermatozoa is superior compared with seminal and postswim-up samples, no difference among the percentage of the normal morphology in different microdroplets could be demonstrated. CONCLUSION: Optimal volume for the obtained concentration of spermatozoa from a patient can be calculated and therapeutically used for cases of severe oligozoospermic patients by microvolume inseminations in IVF practice.

Female↗

PubMed1995

Comparison of two predictive models for prognosis in critically ill patients in a Veteran's Affairs Medical Center coronary care unit.

STUDY OBJECTIVE: The acute physiologic and chronic health evaluation score has been developed to assess prognosis in critically ill patients. Knaus et al initially determined and validated diagnosis-specific coefficients for prediction of outcome in a group of multidisciplinary ICUs. Teskey et al found different coefficients for cardiac diagnoses in a retrospective analysis of coronary care unit (CCU) only patients. This study compares the actual mortality in a Veteran's Affairs Medical Center (VAMC) CCU with the mortality predicted by the two equations. DESIGN AND SETTING: Data were prospectively collected for patients admitted to the medical CCU at a university-affiliated, tertiary care VAMC. PATIENTS: Patients (n = 338) admitted to the CCU with the diagnoses of coronary artery disease (CAD), myocardial infarction (MI), congestive heart failure (CHF), arrhythmia (Arr), and other cardiac-related diagnoses. RESULTS: The entire CCU population showed no significant differences from either the predictions by Knaus et al and Teskey et al in 1991. However, when specific disease states were analyzed as a whole, significant differences from both prediction models for CAD and MI actual mortality were found. Teskey et al was a better predictor for the Arr and CHF population, while both were equally reliable for other. CONCLUSIONS: Either prediction model is reliable for a CCU population in general. However, diagnosis-specific coefficients of Teskey et al appear to correlate better with actual mortality for this VAMC. Significant outcome differences compared with those predicted may reflect the patient population specific to a VAMC.

APACHE↗

PubMed2000

[Development of a predictive model for respiratory isolation of patients suspected of having pulmonary tuberculosis].

INTRODUCTION: As numerous nosocomial outbreaks of pulmonary tuberculosis have been reported during the last two decades, prompt identification and effective isolation of contagious patients should be made a priority in tuberculosis control policies. There is a need to develop a predictive model which would allow prompt recognition and isolation of smear-positive patients. CURRENT KNOWLEDGE AND KEY POINTS: Various authors have attempted to improve the respiratory isolation policies for patients suspected of having pulmonary tuberculosis. A French multicenter prospective study of 211 patients suspected of having pulmonary tuberculosis established that: 1) the current respiratory isolation policy of suspected pulmonary tuberculosis needs improvement (sensitivity = 71.4%; i.e., 28.6% of smear-positive patients are admitted without isolation) and 2) better interpretation of clinical and radiological data available on patient admission could improve the adequacy of respiratory isolation. Univariate analysis showed that predictive factors of pulmonary tuberculosis were chest X-rays (P < 0.00001), symptoms (P = 0.0004), age (mean: 40.8 years for TB vs. 47.5 for non-TB, P = 0.04), HIV infection (10.6% vs. 28.7%, P = 0.01), immigrant (72% vs. 55%, P = 0.03) and BCG status (P = 0.025), while multivariate analysis demonstrated that chest X-ray pattern (P < 0.00001), HIV infection (P = 0.002) and symptoms (P = 0.009) were independent predictive factors. FUTURE PROSPECTS AND PROJECTS: From these data, a model was proposed and evaluated in the derivation cohort using the receiver operating characteristics (ROC) curve. We retrospectively studied the predictive model in two populations different from the one from which it was derived. The model would have improved sensitivity of the respiratory isolation policy from 71.4% (current respiratory isolation policy) to 82.4% and 91.1%, respectively. Prospective, multicenter studies are requested to establish the value of such a predictive model in improving the respiratory isolation policy for patients suspected of having pulmonary tuberculosis.

Cohort Studies↗

PubMedDate not supplied

Posterior predictive model checks for disease mapping models.

Disease incidence or disease mortality rates for small areas are often displayed on maps. Maps of raw rates, disease counts divided by the total population at risk, have been criticized as unreliable due to non-constant variance associated with heterogeneity in base population size. This has led to the use of model-based Bayes or empirical Bayes point estimates for map creation. Because the maps have important epidemiological and political consequences, for example, they are often used to identify small areas with unusually high or low unexplained risk, it is important that the assumptions of the underlying models be scrutinized. We review the use of posterior predictive model checks, which compare features of the observed data to the same features of replicate data generated under the model, for assessing model fitness. One crucial issue is whether extrema are potentially important epidemiological findings or merely evidence of poor model fit. We propose the use of the cross-validation posterior predictive distribution, obtained by reanalyzing the data without a suspect small area, as a method for assessing whether the observed count in the area is consistent with the model. Because it may not be feasible to actually reanalyze the data for each suspect small area in large data sets, two methods for approximating the cross-validation posterior predictive distribution are described.

Algorithms↗

PubMed2003

Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model.

BACKGROUND: Models based on artificial neural networks (ANN) are useful in predicting outcome of various disorders. There is currently no useful predictive model for risk assessment in acute lower-gastrointestinal haemorrhage. We investigated whether ANN models using information available during triage could predict clinical outcome in patients with this disorder. METHODS: ANN and multiple-logistic-regression (MLR) models were constructed from non-endoscopic data of patients admitted with acute lower-gastrointestinal haemorrhage. The performance of ANN in classifying patients into high-risk and low-risk groups was compared with that of another validated scoring system (BLEED), with the outcome variables recurrent bleeding, death, and therapeutic interventions for control of haemorrhage. The ANN models were trained with data from patients admitted to the primary institution during the first 12 months (n=120) and then internally validated with data from patients admitted to the same institution during the next 6 months (n=70). The ANN models were then externally validated and direct comparison made with MLR in patients admitted to an independent institution in another US state (n=142). FINDINGS: Clinical features were similar for training and validation groups. The predictive accuracy of ANN was significantly better than that of BLEED (predictive accuracy in internal validation group for death 87% vs 21%; for recurrent bleeding 89% vs 41%; and for intervention 96% vs 46%) and similar to MLR. During external validation, ANN performed well in predicting death (97%), recurrent bleeding (93%), and need for intervention (94%), and it was superior to MLR (70%, 73%, and 70%, respectively). INTERPRETATION: ANN can accurately predict the outcome for patients presenting with acute lower-gastrointestinal haemorrhage and may be generally useful for the risk stratification of these patients.

Acute Disease↗

PubMed1995

Comparison of two prognostic models predicting survival in patients with malignant melanoma.

Accurate predictions of prognosis are important in the clinical management of patients with malignant melanoma. Primary lesions from 55 patients with cutaneous melanoma, having 10 or more years of clinical follow-up, were evaluated by two models predicting patient survival. One model was simple and relied solely on tumor thickness. The other model was complex and considered stage of tumor progression, and six clinical and histological variables. Accuracy of the two models was determined by retrospective review of the medical record, and the predictive power of each model was compared by receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (a measure of the "goodness" of the model) for the single variable model was 0.70 +/- 0.0775 standard error (SE), and the area under the ROC curve for the multiple variable model was 0.77 +/- 0.0779 SE. Although a modest improvement in predictive power is suggested for the multiple variable model, the SEs for the two models overlap, and the difference is not statistically significant. Further study using a larger database may be required to determine definitively if the multiple variable model significantly increases the ability to predict patient survival or death, and make better clinical decisions.

Adult↗

PubMed2007

Predictive model for the outcome of infliximab therapy in Crohn's disease based on apoptotic pharmacogenetic index and clinical predictors.

BACKGROUND: Infliximab (IFX) is an effective therapy for refractory luminal and fistulizing Crohn's disease (CD). Predictors of response could improve selection of patients with a higher probability of favorable outcomes and could improve the safety profile. We aimed to develop a predictive model for the response to infliximab in CD. METHODS: Genetic and clinical data collected in a previous pharmacogenetic study of apoptosis genes were analyzed using SAS Enterprise miner modeling software and SPSS 12.0. We proposed a novel apoptotic pharmacogenetic index (API) with a score ranging from 0 (low apoptotic response) to 3 (high apoptotic response) and subsequently developed a decision tree model. RESULTS: Response and remission rates significantly increased with API score (P = 0.005 in the group of patients with luminal CD, P = 0.02 in the group of patients with fistulizing CD). Patients with an API < or = 1 (n = 59) had the lowest response and remission rates in both the luminal CD (50% and 39.5%, respectively) and fistulizing CD (61.9% and 28.6%, respectively) groups, compared to those with an API of 2 (n = 158), whose response and remission rates were 73.8% and 56.1%, respectively, in the luminal CD group and 85.7% and 44.9%, respectively, in the fistulizing CD group; and those with an API of 3 (n = 10), whose response and remission rates were 100% and 85.7%, respectively, in the luminal CD group and 100% and 0% in the fistulizing CD group. Response in patients with an API < or = 1 was significantly influenced by concurrent azathioprine therapy in the luminal CD (21.4% versus 78.9%, P < 0.001) and in the fistulizing CD (46.6% versus 100%, P = 0.04) groups. In patients with an API of 2, we saw an interaction with age older than 40 years and location of disease (response 52.2% versus 83.9%, P = 0.008) in the luminal CD group and with baseline CRP greater than 5 mg/L (73.9% versus 93.9%, P = 0.04) in the fistulizing CD group. CONCLUSIONS: From our newly proposed apoptotic pharmacogenetic index and clinical predictors, we developed a model for prediction of low, medium, and high responses to the first infusion of IFX in patients with CD. Further studies are needed to confirm the hypothesis generated by our study.

Adult↗

PubMed2003

Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables.

A model-based control methodology was developed for automated regulation of mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The control algorithm used a multiple-model adaptive approach in a model predictive control framework to account for variability and explicitly handle drug rate constraints. The controller was experimentally evaluated on canines that were pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output. The controller performed better as compared to experiments on manual regulation of the hemodynamic variables. After the model bank was determined, mean arterial pressure was held within +/- 5 mm Hg 88.9% of the time with a standard deviation of 3.9 mm Hg. The cardiac output was held within +/- 1 l/min 96.1% of the time with a standard deviation of 0.5 l/min. The manual runs maintain mean arterial pressure only 82.3% of the time with a standard deviation of 5 mm Hg, and cardiac output 92.2% of the time with a standard deviation of 0.6 l/min.

Algorithms↗

PubMed1999

Radioactive contamination: state of the science and its application to predictive models.

Information on environmental levels and transport processes of natural and anthropogenic radioactivity, although plentiful, is widely scattered, and relatively few attempts have been made to summarize and synthesize this information. Furthermore, most experimental observations and experiments on environmental radioactivity have been designed for documentation or testing of specific hypotheses, rather than for providing key information for transport simulation models or on fundamental processes which such models seek to represent. This paper examines three basic questions, namely (1) what is the current state of the science of radioecology?; (2) how well is this science being incorporated into predictive models?; and (3) how well are the models being used to guide and improve the science? These discussions will be preceded by a brief description of the field of radioecology, and comments on its relevance to other sciences as well as to major societal problems stemming from environmental releases of radioactivity. In addition to assessing the current state of the science and its use in predictive models, specific ideas for improving both the science and its associated models will be advanced. These ideas fall under the categories of (1) environmental transport processes and model parameters, (2) estimating exposure and dose to human and ecological receptors, and (3) dose-effect relationships for plants and animals.

Journal Article↗