EXPERIENCED PHYSICIANS BENEFIT FROM ANALYZING INITIAL DIAGNOSTIC HYPOTHESES
Bibliographic record
Abstract
Background: Most incorrect diagnoses involve at least one cognitive error, of which premature closure is the most prevalent. While metacognitive strategies can mitigate premature closure in inexperienced learners, these are rarely studied in experienced physicians. Our objective here was to evaluate the effect of analytic information processing on diagnostic performance of nephrologists and nephrology residents. Methods: We asked nine nephrologists and six nephrology residents at the University of Calgary and Glasgow University to diagnose ten nephrology cases. We provided presenting features along with contextual information, after which we asked for an initial diagnosis. We then primed participants to use either hypothetico-deductive reasoning or scheme-inductive reasoning to analyze the remaining case data and generate a final diagnosis. Results: After analyzing initial hypotheses, both nephrologists and residents improved the accuracy of final diagnoses (31.1% vs. 65.6%, p < 0.001, and 40.0% vs. 70.0%, p < 0.001, respectively). We found a significant interaction between experience and analytic processing strategy (p = 0.002): nephrology residents had significantly increased odds of diagnostic success when using scheme-inductive reasoning (odds ratio [95% confidence interval] 5.69 [1.59, 20.33], p = 0.007), whereas the performance of experienced nephrologists did not differ between strategies (odds ratio 0.57 [0.23, 1.39], p = 0.2). Discussion: Experienced nephrologists and nephrology residents can improve their performance by analyzing initial diagnostic hypotheses. The explanation of the interaction between experience and the effect of different reasoning strategies is unclear, but may relate to preferences in reasoning strategy, or the changes in knowledge structure with experience.
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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 | 0.008 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".