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PubMed2026

Spinal meningiomas: histopathological grading using a benchmark radiomics model with notes on disease control.

OBJECTIVE: Spinal meningiomas (SMs) are common primary spinal tumors for which surgery is considered the first-line treatment when safe and feasible. The ability to extrapolate the tumor grade from preoperative imaging may significantly inform early patient expectation-setting regarding recurrence. Building on radiomics studies in cranial meningiomas, the authors aimed to construct a benchmark radiomics model to preoperatively identify the histological grade of SMs. METHODS: Institutional surgical records from May 2012 to November 2025 were queried for pathology-confirmed meningiomas below the foramen magnum, with preoperative contrast-enhanced imaging available for segmentation. SMs were classified as low-grade (WHO grade 1) and high-grade (WHO grade 2 tumors and grade 1 tumors with atypia). Tumors were manually segmented, and features were extracted using the PyRadiomics software package. An ensemble model of k-nearest neighbors, random forest, and support vector machine classifiers was trained using nested cross-validation on a subset of 10 features to differentiate tumor grades. Clinical data for the cohort were also extracted, and disease control in an adjunctive clinical series was assessed. RESULTS: Seventy-four patients were included in radiomics analysis, with an area under the receiver operating characteristic curve of 0.879 and a mean F1 score of 0.748. The model's top 5 features were all texture features that differed significantly (p < 0.05) across low- and high-grade SMs. These included measures of tumor textural and contrast-enhancement heterogeneity, with overlap with features reported in radiomics models for histological grading of intracranial meningiomas. Fifty-five patients with a median radiographic follow-up of 22.2 (range 1.9-86.4) months remained for clinical analysis after exclusion of patients with less than 1 month of follow-up and syndromic meningiomas. Four recurrences occurred at a median of 20.8 (range 1.8-41.8) months. High-grade tumor pathology did not significantly impact progression-free survival (p = 0.682, log-rank test; Cox regression high vs low grade hazard ratio [HR] 0.62, 95% CI 0.06-6.11, p = 0.685). Subtotal resection was associated with poorer progression-free survival than gross-total resection (p = 0.004, log-rank test; Cox regression subtotal vs gross-total resection HR 10.62, 95% CI 1.46-77.05, p = 0.019). These findings remain contextualized within a relatively limited follow-up window and small recurrence event count, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs. CONCLUSIONS: A preoperative radiomics model can stratify high-grade SMs using open-source tools applied to single-institution data.

Humans↗

PubMed2026

Identification of Critical Genes for Recurrent Aphthous Ulcer by Transcriptome Data Analysis and Mendelian Randomization.

PURPOSE: Recurrent aphthous ulcer (RAU) is a common oral mucosal disorder with a poorly understood etiology, significantly affecting patients' quality of life. This study aims to investigate critical genes linked to RAU and explore their biological mechanisms using transcriptomic data and Mendelian randomization (MR) analysis. MATERIALS AND METHODS: RAU-related gene expression data from the GEO database (GSE37265) were analyzed to identify differentially expressed genes (DEGs). A two-sample MR approach was used to assess the causal impact of expression quantitative trait loci (eQTL) on RAU. Critical genes were identified by intersecting DEGs with significant MR findings. GO and KEGG pathway enrichment analyses were performed, along with GSEA and immune cell infiltration analysis, to investigate the functions and mechanisms of these genes in RAU. RESULTS: A total of 184 differentially expressed genes (DEGs) were identified, while 339 RAU-associated genes were screened through MR analysis. Cross-validation further identified 7 critical genes. Among these, CCR1, ERP27, HCK, MICB, and SLC2A3 showed protective associations with RAU risk, whereas CD177 and IFITM1 were positively associated with increased risk. Enrichment analysis revealed that these genes are involved in specific biological processes, including cell migration, immune response, and metabolic regulation, which are closely linked to RAU pathogenesis. CONCLUSION: This systematic study comprehensively investigates the critical causative genes underlying RAU, emphasizing the intricate relationships between immune regulation and metabolic disturbances in its pathology. These findings lay a solid foundation for the development of novel biomarkers and may inform future research on targeted therapeutic strategies for RAU.

Stomatitis, Aphthous↗

PubMed2026

Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.

OBJECTIVE: To identify plasma protein biomarkers associated with incident non-fatal major adverse cardiovascular events (MACE) in diabetic kidney disease (DKD) patients. RESEARCH DESIGN AND METHODS: We analyzed 317 DKD patients from the UK Biobank. Plasma proteomics and clinical data (demographics, metabolism, renal function) were integrated. In an exploratory discovery phase, three sequential Cox regression models (crude, socio-demographic-adjusted, socio-demographic-metabolic adjusted) screened non-fatal MACE-associated proteins. To prevent information leakage, the cohort was then randomly split into training (70%) and testing (30%) sets; machine-learning feature selection, hyperparameter optimization, and final model development were performed exclusively within the training set. The associated proteins were input into the four-step machine-learning pipeline (LASSO-Cox, random survival forest, Boruta, XGBoost-Cox). Predictive performance was validated using Kaplan-Meier survival analyses, longitudinal trajectory modeling, and ROC benchmarking. An interactive web application was deployed for clinical implementation. RESULTS: Of 1,463 plasma proteins, 561 were associated with non-fatal MACE across Cox models, with 14 overlapping proteins. Nine core proteins (ANG, IL1R1, CXCL14, ESAM, PTGDS, HAVCR1, FGFR2, IGSF8, CCL3) were validated: ANG showed the strongest non-fatal MACE association (HR = 3.88, 95%CI 2.33-6.48, p<0.001), and all high-expression groups had elevated non-fatal MACE risk. GO/KEGG enrichment highlighted inflammatory-immune pathways like positive regulation of MAPK cascade, Cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway as key mechanisms. The model integrating proteins, demographic factors, and clinical variables achieved the highest predictive performance across non-fatal MACE (AUC = 0.768), myocardial infarction (MI) (0.808), and stroke (0.816) outcomes, with superior stability in cross-validation. CoxBoost + Elastic Net framework was selected as the optimal framework via benchmarking of 101 algorithms. The model demonstrated favorable calibration in high-risk patients and yielded positive net clinical benefit across decision thresholds of 5% to 45%. The web tool (https://jiangli2941.github.io/MACE-prediction-v2/) enables input of 28 variables, outputs non-fatal MACE risk status, risk probability, and highlights abnormal indicators. CONCLUSION: Plasma proteomics combined with machine learning identifies robust non-fatal MACE predictors in DKD.

Humans↗

PubMed2026

Machine learning-integrated multi-omics risk prediction for pulmonary fungal infection in COPD and lung cancer: a transcriptomic and immune profiling study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and lung cancer are major risk factors for invasive pulmonary fungal infection (IPFI), carrying an attributable mortality of 30%-80%. Their coexistence further amplifies immunosuppression, while current diagnostic criteria remain inadequate for early risk identification. METHODS: Transcriptomic data from the GEO dataset GSE296912 (scRNA-seq; 12,078 cells from normal and COPD lung tissue) and The Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) bulk RNA-seq cohort (539 tumor and 59 normal samples) underwent differential expression and cross-omics integration analysis. Five machine learning models were constructed: logistic regression, SVM, random forest, XGBoost, and LASSO. Candidate genes were validated by qRT-PCR in A549 cells and THP-1-derived macrophages stimulated with heat-inactivated Aspergillus fumigatus conidia, a protocol selected to ensure BSL-2 biosafety compliance and isolate PAMP-mediated innate immune signaling. Model performance was evaluated using 5-fold stratified cross-validation with AUC, calibration curves, and decision curve analysis. RESULTS: Single-cell transcriptomic analysis of 12,078 cells identified 14 distinct cell populations, with marked myeloid expansion and immune dysregulation in COPD lung tissue. Cross-omics integration with TCGA-LUAD data identified 1,145 shared genes (79 immune-related), converging on NF-κB, TLR4, and cytokine receptor signaling. The random forest model achieved excellent discriminative performance (5-fold CV AUC = 0.988), with Treg infiltration, TLR4, and MMP9 as the top predictors. qRT-PCR confirmed significant upregulation of all five candidate genes (DEFB4A, S100A8, IL-8, MMP9, and TLR4) in both A549 and THP-1 cells following fungal stimulation. CONCLUSION: This multi-omics machine learning model integrating scRNA-seq and TCGA transcriptomic data demonstrates excellent discriminative performance (AUC = 0.988), with mechanistic convergence of NF-κB, TLR4, and oncogenic signaling pathways identified across shared immune gene signatures. In vitro qRT-PCR validation confirms the biological relevance of five key antifungal immune genes, providing a transcriptomic foundation for future prospective IPFI risk stratification in patients with COPD and lung cancer.

TLR4↗

PubMed2026

Comparative genomics reveals hidden biosynthetic diversity in Streptomyces spp. and metal-dependent regulatory features associated with untapped specialized metabolites.

The genus Streptomyces is one of the richest sources of bioactive natural products; however, a substantial proportion of its biosynthetic gene clusters (BGCs) remain cryptic and their metabolic products are unresolved. Advances in genome mining and computational prediction now enable comprehensive exploration of this hidden biosynthetic repertoire. In this study, whole-genome sequencing and comparative genomic analyses were performed on three three newly isolated Streptomyces strains to evaluate their specialized metabolic potential. Genome assemblies were annotated and systematically analyzed using antiSMASH, DeepBGC, GECCO, and PRISM to identify, cross-validate, and functionally characterize BGCs while predicting their associated secondary metabolite scaffolds. Taxonomic analyses based on Average Nucleotide Identity (ANI), phylogenomics, and BLAST identified the isolates as Streptomyces thinghirensis, Streptomyces novocaesareae, and Streptomyces griseorubens. Applying the consensus framework across the three Streptomyces genomes yielded 43 cryptic BGCs, lacking close similarity to reference BGCs in the MIBiG database, of which 26 were classified as HIGH, 10 as MEDIUM, and 7 as LOW confidence. Notably, numerous BGCs exhibited low abundance to characterized reference clusters, indicating a high potential for previously undescribed biosynthetic pathways and novel metabolite scaffolds. Comparative analyses further revealed strain-specific biosynthetic architectures together with putative metal-responsive regulatory systems; Fur, Zur, and Nur, which were frequently associated with specialized metabolite biosynthetic loci. Collectively, these findings demonstrate the effectiveness of integrated genome-mining strategies for prioritizing cryptic biosynthetic gene clusters and highlight the remarkable biosynthetic potential of newly identified Streptomyces isolates as a source of novel natural products.

comparative genomics↗

PubMed2026

Discovery and validation of GNA12circle as a first-trimester plasma eccDNA marker for early-onset preeclampsia.

BACKGROUND: Early-onset preeclampsia (EOPE) is a major cause of maternal and perinatal morbidity and is characterized by placental dysfunction, systemic endothelial injury, and hypertensive vascular stress. Because hypertensive disorders of pregnancy may also signal later maternal cardiovascular and cerebrovascular vulnerability, effective biomarkers for first-trimester risk assessment remain clinically important. Extrachromosomal circular DNA (eccDNA), a stable form of circulating cell-free DNA, has emerged as a potential source of disease-associated biomarkers. This study aimed to characterize first-trimester plasma eccDNA alterations associated with subsequent EOPE and to identify and validate a candidate circulating eccDNA marker for early risk assessment. METHODS: A two-stage nested case-control study was conducted within a prospective birth cohort. In the discovery stage, plasma samples collected at 11-13 weeks of gestation from 5 women who subsequently developed EOPE and 5 matched normotensive controls were profiled by Circle-Seq to characterize genome-wide eccDNA alterations. Candidate eccDNAs were prioritized through differential abundance analysis and were further confirmed by outward PCR and Sanger sequencing. In the validation stage, the candidate selected marker was quantified by junction-specific qPCR in an independent cohort of 109 EOPE cases and 109 controls. Its potential predictive value was further evaluated alone and in combination with routine first-trimester clinical variables. RESULTS: In the exploratory discovery analysis, 410 nominally differentially abundant candidate eccDNAs were identified as a hypothesis-generating pool. Among these, GNA12circle (chr7:2876332-2,876,692) was prioritized and experimentally validated at the circular junction. In the independent validation cohort, plasma GNA12circle levels were significantly higher in women who later developed EOPE than in controls. When combined with routine first-trimester variables, GNA12circle improved predictive performance. The RF model showed the best overall cross-validated performance among the evaluated classifiers, with a mean held-out test-fold AUC of 0.843. CONCLUSION: First-trimester plasma eccDNA profiling revealed distinct alterations associated with subsequent EOPE, from which GNA12circle was identified and validated as a candidate circulating marker. These findings support further investigation of circulating eccDNA for early EOPE risk assessment in larger multicenter populations.

Humans↗

PubMed2026

Genetic Susceptibility to Incisional Hernia Evaluation of Hernia Polygenic Risk Scores.

OBJECTIVES: Incisional hernia (IH) affects 13-30% of people after abdominal surgery, resulting in substantial morbidity and costs. While clinical risk factors have been studied extensively, genomic risk for IH is incompletely understood. We aimed to evaluate the impact of polygenic risk scores (PRS) on IH risk prediction. METHODS: We created and evaluated three PRS for abdominal hernia, ventral hernia and latent hernia susceptibility for prediction of IH in an institutional biobank. The primary outcome was defined as the diagnosis or repair of an IH based on ICD-9/10-CM/PCS and CPT codes. Clinical covariates included age, sex, body mass index (BMI), smoking status, index procedure type, and perioperative surgical site infection. A phenome-wide association study (PheWAS) was performed to assess clinical associations with increased PRS. We then tested the ability of the PRS to improve prediction for IH by modeling clinical covariates with and without PRS in patients who underwent abdominal surgery. Model performance was assessed using 10 iterations of 5-fold cross-validation to estimate Brier scores and area under the receiver operating characteristic curve (AUROC), which were compared using cross-model Bayesian analysis of variance. RESULTS: In 55,809 subjects, assessed PRS was significantly associated with incisional, umbilical, and ventral hernia on PheWAS, with 1.19 greater odds of developing IH per 1-SD increase in PRS (95% CI: 1.13-1.25, P < 0.001). Of 9,909 subjects who underwent qualifying abdominal surgery, 706 developed IH. In this cohort, the latent hernia susceptibility PRS was associated with a 16% increased hazard of developing IH per 1-SD increase (HR 1.16; 95% CI: 1.07-1.26; P < 0.001). Compared to a predictive model using clinical covariates (Brier score = 0.047, 95% CI: 0.046-0.048; AUROC = 0.660, 95% CI: 0.653-0.666), addition of the PRS showed similar Brier score and AUROC estimates (Brier score = 0.047, 95% CI: 0.046-0.048; AUROC: 0.667, 95% CI: 0.661-0.673) at five years. Cross-model Bayesian analysis demonstrated >99% probability of practical equivalence when trying to detect a difference of ≥ 0.02. CONCLUSION: All three PRS for hernia were independently associated with IH, suggesting that genomic factors contribute significantly to IH development. However, none of the three PRS meaningfully improved clinical IH risk prediction in patients who underwent abdominal surgery. This suggests that clinical comorbidities and surgical techniques may be equally as important as genomic architecture.

Bayesian analysis↗

PubMed2026

Genomic signatures associated with epidemiologically defined high-risk pathogenic Escherichia coli isolates identified by interpretable machine learning.

Pathogenic Escherichia coli is a major cause of foodborne illness worldwide and includes strains capable of causing severe disease. To establish a genome-informed framework for foodborne outbreak surveillance, we analyzed 1,029 E. coli isolates from clinical, food, livestock, and environmental sources using whole-genome sequencing. Pathogenic isolates obtained from human clinical cases or linked to documented outbreaks were classified as epidemiologically defined high-risk (EpiHR), whereas the remaining pathogenic isolates were classified as non-EpiHR. Virulence-associated genomic features were extracted using a bioinformatics pipeline, and four machine learning (ML) algorithms, including gradient boosting machine, random forest (RF), and support vector machines with linear and radial basis function kernels, were evaluated. Among them, the RF model showed the best performance, achieving an area under the curve (AUC) of 0.98 and accuracy of 0.93 in 10-fold cross-validation. Additional leave-one-group-out validation showed retained discrimination across held-out sequence types and serotypes, although performance was reduced when isolates were grouped by isolation source. Evaluation using an independent test dataset of 1,908 publicly available pathogenic E. coli genomes showed an AUC of 0.97 and a sensitivity of 0.98. Feature importance analysis using Shapley additive explanations identified influential predictive features, including traT, etpB, and enterotoxin-associated genes. A reduced 10-feature model achieved an AUC of 0.79 in the independent test dataset, supporting its exploratory use for future simplified screening approaches. These results indicate that genome-based ML provides a sensitive framework for surveillance-oriented prioritization of EpiHR pathogenic E. coli isolates, with model predictions interpreted together with epidemiological information.

Escherichia coli↗

PubMed1975

A performance rating scale for evaluating clinical competence of occupational therapy students.

This study was conducted to assess the effectiveness of a 53-item rating scale, the Field Work Performance Report (FWPR), developed to evaluate the performance of occupational therapy students during field work. University faculty and field work supervisors from five different geographic regions in the United States participated in instrument construction. The FWPR was field-tested on a standardization group of 934 student affiliates. An item-analysis and cross-validation design was used to investigate instrument reliability and validity. When FWPR ratings were correlated with other supervisor ratings of student performance, validity coefficients for this scale ranged from .62 to .83. The tests of reliability yielded an inter-rater correlation coefficient of .75, where the consistency coefficient was .97. The FWPR has now been adopted as the official instrument of the AOTA for evaluation of field work performance.

Achievement↗

PubMed1975

Factors in 171 navy diving decompression accidents occurring between 1960-1969.

Comparisons were made between the incidence of specific factors in U.S. Navy decompression accidents and the incidence of these factors in routine (nonexperimental) U.S. Navy operational dives. It was found that decompression accidents are disproportionately high among a) air dives less than 140 ft which have bottom times of 30 min or less and air dives greater than 140 ft which have bottom times of more than 15 min, b) Divers First Class, c) older divers, and d) dives which do not involve work or divers which require heavy work. Repetitive dives have a lower decompression accident rate than expected. Decompression accidents were not disproportionately high for any category of body build. These results indicate that the present U.S. Navy decompression tables are extremely safe (5 decompression accidents/10,000 dives), and do not appear to require modification. Future decompression research may be directed toward analyzing the relationship of work and aging to physiological processes involved in decompression. In addition, the present findings should be cross-validated using more recent accident and operational diving data.

Accidents↗

PubMed1992

Estimation of peak oxygen consumption from a sub-maximal half mile walk in obese females.

This study compared peak oxygen consumption estimated from a half mile sub-maximal walk test with oxygen consumption determined from a maximal treadmill test with 96 obese females. A half mile walk was completed as quickly as possible without running. Retests on 71 subjects were completed to determine the reliability of the half mile walk. Hearts rates were obtained for 15 s immediately upon completion of the walks. Stepwise regression to estimate peak VO2 was used with the following variables: walk time (TM), body weight, height, BMI, age, and heart rate (HR). The best equation was peak VO2 (ml/kg/min) = 53.23 - (1.98 x TM) - (0.32 x BMI) - (0.08 x age), r = 0.76, s.e.e. = 2.89 ml/kg/min. The mean difference between determined and estimated peak VO2 in a cross-validation group of subjects who were not included in the validation group was 0.15 ml/kg/min (n.s.), r = 0.77, s.e.e. = 3.01 ml/kg/min, E = 3.09. Reliability for time to complete the half mile walk was r = 0.87 with 0.1 min difference (n.s.). This study indicated the half mile walk to provide an accurate, reliable estimate of peak VO2 which should be useful in the clinical setting.

Adult↗

PubMed1992

Parental and professional perceptions of problems associated with long-term pediatric home tube feeding.

We surveyed 24 caregivers of children fed by gastrostomy tube to identify day-to-day problems encountered in home enteral nutrition. In a second survey, 21 respondents cross-validated the problem list by rating how often they experienced each problem and how difficult each problem was to manage. Problems related to social functioning, recreational activities, and family functioning were rated by caregivers as the most frequent and difficult. We asked 11 pediatric feeding specialists to rate the same problems based on their perceptions of the frequency and severity of problems experienced by caregivers. Moderate agreement was found between professional and caregiver ratings, although professionals generally rated problems as more common and difficult than did the caregivers. Additionally, positive relationships were found between caregiver ratings of common gastrostomy-tube problems and general levels of stress in the home. Data suggest that problems identified by caregivers as most common and difficult are often in the social rather than the medical realm. A broadly based family assessment that focuses on medical and social aspects of home enteral nutrition may maximize nutritional benefits for the patient as well as improve general family functioning and reduce stress in the home.

Adolescent↗

PubMed1992

The application of backpropagation neural networks to problems in pathology and laboratory medicine.

Neural networks are a group of computer-based pattern recognition technologies that have been applied to problems in clinical diagnosis. This review focuses on one member of the group of neural networks, the backpropagation network. The steps in creating a backpropagation network are (1) collecting adequate training facts, (2) choosing the specific network structure, (3) training the network, and (4) cross-validating the trained network. The first published applications of backpropagation networks to problems in pathology and laboratory medicine have appeared recently. These applications are in the areas of image analysis and interpretation of laboratory results, and they demonstrate the feasibility of the approach.

Evaluation Studies as Topic↗

PubMed1992

Applying psychometric criteria to functional assessment in medical rehabilitation: III. Construct validity and predicting level of care.

A discriminant analysis was performed using Rasch ability estimates derived from four Patient Evaluation and Conference System (PECS) subscales, to distinguish among the functional independence and impairment profiles exhibited by patients admitted into any of three levels of medical rehabilitation delineated by the National Association of Rehabilitation Facilities (NARF): (1) inpatient hospital, (2) atypical nursing home (intermediate), or (3) day program. Two discriminant functions accounted for 91% and 9% of the between-group variance, respectively. Cross-validated classification of patients into one of the three levels of care, based on discriminant function scores, produced 75% correct classification; a 66% improvement over the percentage of correct classification likely by chance alone. Results support the construct validity of the PECS subscales and indicate they may be useful in validating clinically-based admission decisions among three of the levels of care promulgated by NARF.

Activities of Daily Living↗

PubMed1992

Blocked arteries and multivariate regression.

Ultrasound blood flow waveforms may be used in the diagnosis of arterial occlusive disease in human legs. We develop a statistical model to predict disease severity, conditional on the ultrasound data and some training data. It belongs to the class of models known as seemingly unrelated regressions, for which the Bayesian predictive density function cannot be evaluated analytically. Allowing for missing components of response vectors in the training data, we describe a first-order approximation to the predictive density, based on a Bayes estimate of the precision matrix. This approximation is then used to generate cross-validated predictions of disease severity in a set of 31 patients. We conclude with a discussion of the results.

Arterial Occlusive Diseases↗

PubMed1979

Individual temperament as a predictor of health or premature disease.

Two studies of temperament as a possible predictor of continuing good health or premature disease are reported. In earlier work to determine youthful precursors of premature disease, a number of separate characteristics distinguishing medical students who remain healthy from those with premature disorders have been identified. Characterization by temperament, an expression of innate biological endowment, provides a more global portrayal of an organism that can an aggregate of separate characteristics alone. Criteria for designating three temperament types, termed Alpha, Beta and Gamma, are presented. In 1948, 45 subjects were assigned to one of these types on the basis of youthful characteristics. The subjects in the three temperament groups had different health outcomes 30 years later, significant at the p less than 0.01 level. Gamma type had the most disorders and deaths, Beta type the fewest. A cross-validation study on 127 subjects had similar results at the p less than 0.05 level. Temperament appears to be a variable of predictive potential of individual stamina, or of vulnerability to premature disease and death.

Adult↗

PubMed1979

Prediction of maximal oxygen uptake in boys, ages 7-15 years.

Predictions of Vo2max (1 min-1 and ml kg-1 min-1) were obtained via multiple regression procedures from a sample of 100 boys, ages 6.7-14.8 years. Prediction equations for Vo2max (1 min-1) were obtained from the subjects' height, and the Vo2 (1 min-1) and heart rate observed during the third min of a treadmill walk (R = 0.95; CV = +/- 9.3%). A similar prediction was obtained when the subjects' age, height and weight were used (R = -0.94; CV = +/- 9.7%). Vo2max (ml kg-1 min-1) was predicted with similar accuracy (CV = +/- 8.4%) from age, and heart rate, VCO2(1 min-1), Vo2 (ml kg min-1), or from simply age, height and weight (CV = +/- 9.2%). Cross-validation of the equations with another sample of 39 boys demonstrated that the prediction equations based on laboratory data were quite stable, % errors approximately 1-2 +/- 9%. However, the equations based on age, height and weight underestimated the Vo2max slightly, both in 1 min (X = -0.091 min) and ml kg min (X = -2.2 ml kg min, P less than 0.05). The results indicate that reasonably reliable and accurate estimates of Vo2max for children may be obtained from either laboratory data, or simply from their age, height and weight.

Adolescent↗

PubMed1979

Alcoholism-related content in the MMPI: item analysis of alcoholics vs. normal and general psychiatric populations.

An analysis of alcoholism-related content in the MMPI was undertaken using an alcoholic population, a psychiatric population and two normal populations. The alcoholic and psychiatric groups were drawn from facilities in the Minneapolis-St. Paul area, and the normal populations were the Hathaway Adult Group and the Mayo Clinic nonpsychiatric patient group. A derivation study and a cross-validation resulted in the identification of three item pools: 1) that discriminate alcoholics from both normals and psychiatric patients, 2) items that discriminate alcoholics from normals only, 3) items that discriminate alcoholics from psychiatric patients only. Only seven items discriminate both male and female alcoholics from both normals and psychiatric patients. These items have substantial face validity. As the core of alcoholism-related content in the MMPI, they can serve as a stem for the development of scales with more specialized purposes.

Alcoholism↗