PubMed HealthSearch

Biomedical subjects

R Centor

Publications and source records attributed to R Centor.

4 recordsLinked to original sources

Impact of patient history on residents' evaluation on child sexual abuse.

OBJECTIVE: To determine if historical information influences residents' interpretation of physical findings in sexually abused children. METHODOLOGY: In a pediatric residency training program, all residents viewed 15 slides of children's genitalia (8 normal, 7 abnormal) with either a history specific for sexual abuse or one which was nonspecific. Three weeks later the same slides were viewed but with the alternate history scenario. The residents were asked if the physical findings were specific for sexual abuse. RESULTS: Sixty-four percent of residents completed both surveys. Correct response rate did not vary by gender or year of training. Responses were most often correct when the slide and history were normal (87%). Responses were least accurate when normal historical information was presented with abnormal slides (49%). A logistic regression model demonstrated that residents were less accurate when history and physical did not agree (95% CI = .54- .78). Reexamination of the data using areas under the Receiver Operating Characteristic (ROC) curve confirmed that residents performed on a less accurate ROC curve when the slide and history were incongruent (p < .01). CONCLUSION: Incongruency between patient history and physical exam findings negatively affected this group of residents' ability to discriminate between abuse and nonabuse findings.

Attitude of Health Personnel

Why physicians don't pursue abnormal laboratory tests: an investigation of hypercalcemia and the follow-up of abnormal test results.

For unknown reasons, physicians often ignore unsolicited clinical data. This is thought to impair the quality of medical care and the efficacy of screening programs. To investigate this problem the authors followed 156 consecutive hypercalcemic patients for nine to 15 months. Twenty-eight were lost to follow-up, and the hypercalcemia was ignored in 26. Calcium tests were repeated for 102, and hypercalcemia was confirmed in 53. Of these, 39 were and 14 were not further investigated. Analysis by logistic regression revealed a highly significant relationship between the degree of hypercalcemia and the likelihood that calcium testing would be repeated or that abnormal levels would be further investigated. The authors conclude that, contrary to common opinion, when physicians ignore abnormal laboratory values they are making complex clinical judgments based on the degree of abnormality, the likelihood that further investigation will affect therapy, and the cost of the risk associated with further investigation. Evaluation and attempts to modify this behavior should take into account the complexity of these decisions.

Adult

Stochastic thresholds.

Thresholds have traditionally been represented by a single number; the optimal management of the patient depends on whether his probability of disease is above or below this number. The concept of a threshold as a single number, however, inadequately represents the treatment approach of a group of physicians who do not have all the same threshold or a single physician who is uncertain about the exact value of the threshold. An alternative to a single valued threshold is to consider the threshold as having a probability distribution: for every probability that the patient has the disease there is a probability that the threshold is exceeded. This "stochastic" threshold model contains information about the uncertainty of the threshold estimation. Stochastic thresholds can be useful for testing the sensitivity of a management decision to the patient's probability of disease. They can also be used for comparing the standards of practice of individual physicians or comparing the practice of an individual physician with that of a group.

Decision Making

Measures of the value of a diagnostic test derived from stochastic thresholds.

Previous indices for measuring the potential impact of a diagnostic test on a physician's management of a given patient were derived based on a fixed threshold model. The authors adapted these indices to a stochastic threshold model. In the stochastic threshold model the physician's probability of treating the patient is a function of the patient's probability of disease. From this model the authors derived the management value index (the expected effect that the test has on the physician's probability for treating the patient) and the utility value index (the expected benefit to the patient if the diagnostic test is used). Graphs of the indices versus the patient's probability of disease may be useful in teaching appropriate use of diagnostic tests.

Diagnosis