Do you have to re-examine to reconsider your diagnosis? Checklists and cardiac exam
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated whether clinicians can use checklists to verify their diagnostic decisions. Checklists may improve accuracy by prompting clinicians to reconsider or recollect information but might impair decision making by adding to clinicians' cognitive load. This study assessed whether checklists improve cardiac exam diagnostic accuracy, and whether this benefit is dependent on collecting additional information. METHODS: 191 internal medicine residents examined a cardiopulmonary simulator. They provided a diagnosis, subjective rating of certainty, and key findings before and after using a checklist. Residents were randomised; half were allowed access to the simulator and half were prohibited access to the simulator while using the checklist. Residents rated their cognitive load in each step: prechecklist diagnosis, checklist use and postchecklist diagnosis. RESULT: Verifying with a checklist resulted in improved diagnostic accuracy; 88 residents (46%) made the correct diagnosis before using the checklist compared with 97 (51%) afterwards, p=0.04. The benefit of checklist use was restricted to residents allowed to re-examine the simulator (10 changed to correct diagnosis and one to an incorrect diagnosis) whereas no net benefit was seen among residents unable to re-examine the simulator (two changed to a correct diagnosis and two to an incorrect diagnosis, p=0.03). Those able to re-examine the simulator were slightly more confident after checklist use, whereas those unable to re-examine were slightly less confident after checklist use (p=0.01). The opportunity to re-examine the simulator had no effect on the accuracy of key findings reported. Of the three steps, checklist use was associated with the lowest cognitive load (F1,189=68 p<0.001). CONCLUSIONS: Verifying diagnostic decisions with a checklist improved diagnostic accuracy. This benefit was only seen when more information could be collected. Checklist use was not associated with increased cognitive load.
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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 | 0.010 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".