Biomedical subjects

L S Fried

Publications and source records attributed to L S Fried.

4 recordsLinked to original sources

PubMed1987

Do cardiologists have higher thresholds for recommending coronary arteriography than family physicians?

The purpose of this study was to use a new model of decision making to understand variability in physicians' utilization of diagnostic tests. We studied physicians' recommendations for coronary arteriography in hypothetical patients with chest pain by analyzing responses of 235 cardiologists and family physicians. Thresholds for testing were derived by obtaining estimates of the probability of disease and recommendations for coronary arteriography before and after an exercise test. We found that cardiologists compared with family practitioners had a significantly higher decision threshold and recommended coronary arteriography in fewer patients. These findings suggest that analyzing physicians' decision-making thresholds may be used to characterize differences in the practice behavior of groups of physicians.

Angiocardiography↗

PubMed1986

Distributional expectations and the induction of category structure.

Previous research on how categories are learned from observation of exemplars has largely ignored the possible role of prior expectations concerning how exemplars will be distributed. The experiments reported here explored this issue by presenting subjects with category-learning tasks in which the distributions of exemplars defining the categories were varied. In Experiments 1 and 2 the distributional form of a category was found to affect speed of learning. Learning was faster when a category's distribution was normal than when it was multimodal. Also, subjects in the early stages of learning a multimodal category responded as if it were unimodal. These results suggested that subjects enter category-learning tasks with expectations of unimodal, possibly normal, distributions of exemplars. Experiments 3 and 4 attempted to manipulate subjects' prior expectations by varying the distribution of exemplars in the first of two consecutive category-learning tasks. Learning a multimodal category was influenced by the shape of a previously learned distribution and was facilitated when the earlier distribution was either multimodal or skewed, rather than normal. These results are interpreted as support for a dual-process model of category learning that incorporates the effects of prior expectations concerning exemplar distributions.

Adolescent↗

PubMed1984

Induction of category distributions: a framework for classification learning.

We present a framework for classification learning that assumes that learners use presented instances (whether labeled or unlabeled) to infer the density functions of category exemplars over a feature space and that subsequent classification decisions employ a relative likelihood decision rule based on these inferred density functions. A specific model based on this general framework, the category density model, was proposed to account for the induction of normally distributed categories either with or without error correction or provision of labeled instances. The model was implemented as a computer simulation. Results of five experiments indicated that people could learn category distributions not only without error correction, but without knowledge of the number of categories or even that there were categories to be learned. These and other findings dictated a more general learning model that integrated distributional representations based on both parametric descriptions and stored instances.

Decision Making↗

PubMedDate not supplied

The single-cutoff trap: implications for Bayesian analysis of stress electrocardiograms.

Quantitative analysis of exercise electrocardiograms has been emphasized by many investigators. Specific problems have been found when a single cutoff is used to define a positive or a negative test: a single cutoff does not distinguish stress electrocardiography results that are slightly positive from those that are markedly positive. This may lead clinicians to underweigh strong evidence for or against coronary artery disease. This study evaluated clinicians' quantitative analysis of stress electrocardiograms. Two hundred and thirty-five physicians interpreted the results of mildly positive (1.2 mm ST-segment depression) and strongly positive (2.2 mm ST-segment depression) stress electrocardiograms. Their posttest probability estimates were too high for a mildly positive test (0.62 +/- 0.02 versus actual of 0.38; p less than 0.001) and too low for a strongly positive test (0.77 +/- 0.01 versus actual of 0.98; p less than 0.001). Physicians should understand decision aids and should use multiple rather than single cutoffs to interpret the results of stress electrocardiography.

Bayes Theorem↗