Consistency in diagnostic suggestions does not influence the tendency to accept them
Bibliographic record
Abstract
BACKGROUND: Studies suggest that residents tend to accept diagnostic suggestions, which could lead to diagnostic errors if the suggestion is incorrect. Those studies did not take into account that physicians in clinical practice will mainly encounter correct suggestions. The present study investigated residents' diagnostic performance if they would first encounter a number of correct suggestions followed by a number of incorrect suggestions, and vice versa. It was hypothesized that more incorrect suggestions would be accepted if participants had first evaluated a series of correct suggestions. METHOD: Residents (n = 38) evaluated suggested diagnoses on eight written clinical cases. Half of the participants first evaluated four correct suggestions and then evaluated four incorrect suggestions (C/I condition). The other half started with the four incorrect suggestions followed by the correct suggestions (I/C condition). RESULTS: Our findings show that the evaluation score in the C/I condition (M = 2.87, MSE = 0.14) equaled that in the I/C condition (M = 2.66, MSE = 0.14), F(1,36) = 1.09, p = 0.30, ns, meaning that consistency in preceding suggested diagnoses did not influence the tendency to accept subsequent diagnostic suggestions. There was, however, a significant interaction effect between case order and phase, F(1,36) = 11.82, p = 0.001, η p (2) = 0.25, demonstrating that the score on cases with correct suggestions was higher than the score on cases with incorrect suggestions. CONCLUSION: These findings indicate that consistency in preceding correct or incorrect diagnostic suggestions did not influence the tendency to accept or reject subsequent suggestions. However, overall residents still showed a tendency to accept diagnostic suggestions, which may lead to diagnostic errors if the suggestion is incorrect.
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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 | 0.006 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".