Influence of familiar features on diagnosis: Instantiated features in an applied setting.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical diagnosis can be viewed as a categorization task. There are two mechanisms whereby humans make categorical judgments: "analytical reasoning," based on explicit consideration of features and "nonanalytical reasoning," an unconscious holistic process of matching against prior exemplars. However, there is evidence that prior experience can also operate at the level of individual "instantiated" features (Brooks & Hannah, 2006). The present studies examined instantiated features in medical diagnosis. Four "pseudopsychiatric" conditions, each described by four characteristic features, were taught to undergraduate psychology students. They practiced on additional cases, then were tested on new cases with features from two conditions. In Experiment 1, diagnoses associated with familiar features presented one or three times during practice were assigned a higher probability than those with novel features. Experiment 2 showed that the impact of feature frequency was dependent on its consistency with the case diagnosis. Experiment 3 showed that the effect of feature familiarity was not confined to cases with two equiprobable diagnoses. Experiment 4 showed that the effect remained after a 24 hour delay. These four studies demonstrated that features seen in practice have a greater influence on diagnosis than novel synonyms. In fact, seeing a feature once within the appropriate context (a patient case in which it is a member of the primary diagnosis) was sufficient to form a diagnostic association equivalent to instantiations seen four times in a different context. The results of these studies have implications for theories of categorization and for teaching clinical reasoning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it