Limiting the playing field: does restricting the number of possible diagnoses reduce errors due to diagnosis‐specific feature identification?
Bibliographic record
Abstract
INTRODUCTION: Diagnostic hypotheses influence the identification of clinical features by medical trainees. This influence is strong enough to lead students to interpret features incorrectly if the initial diagnostic suggestion is incorrect. In the present study, we investigated whether reducing the pool of possible diagnoses at the time of test to a few highly plausible alternatives would focus the search for and interpretation of clinical features on a few alternative diagnoses and, as a result, reduce the influence of an initial diagnostic hypothesis on feature identification. METHODS: Naive students were taught 10 electrocardiographic (ECG) diagnoses. At test, they were asked to report all features visible on new ECGs. The test ECGs were presented with the suggestion of a tentative diagnosis (either the correct diagnosis or a plausible alternative) under 2 conditions: students were either instructed that the ECG represented one of 3 possible diagnoses (which were explicitly mentioned), or they were instructed that the ECG might represent any of the 10 diagnoses learned. RESULTS: Students' identification of the ECG features was strongly influenced by the diagnostic suggestion. Reducing the number of alternatives available at the time of test did not reduce the impact of a diagnostic suggestion on feature interpretation. DISCUSSION: Increasing the salience of alternative hypotheses does not reduce the impact of a diagnostic suggestion on the interpretation of clinical features.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".