Checklists improve experts’ diagnostic decisions
Bibliographic record
Abstract
CONTEXT: Checklists are commonly proposed tools to reduce error. However, when applied by experts, checklists have the potential to increase cognitive load and result in 'expertise reversal'. One potential solution is to use checklists in the verification stage, rather than in the initial interpretation stage of diagnostic decisions. This may avoid expertise reversal by preserving the experts' initial approach. Whether checklist use during the verification stage of diagnostic decision making improves experts' diagnostic decisions is unknown. METHODS: Fifteen experts interpreted 18 electrocardiograms (ECGs) in four different conditions: undirected interpretation; verification without a checklist; verification with a checklist, and interpretation combined with verification with a checklist. Outcomes included the number of errors, cognitive load, interpretation time and interpretation length. Outcomes were compared in two analyses: (i) a comparison of verification conditions with and without a checklist, and (ii) a comparison of all four conditions. Standardised scores for each outcome were used to calculate the efficiency of a checklist and to weigh its relative benefit against its relative cost in terms of cognitive load imposed, interpretation time and interpretation length. RESULTS: In both analyses, checklist use was found to reduce error (more errors were corrected in verification conditions with checklists [0.29 ± 0.77 versus 0.03 ± 0.61 errors per ECG], and fewer net errors occurred in all conditions with checklists [0.39 ± 1.14 versus 1.04 ± 1.49 errors per ECG]; p < 0.01 for both). Checklists were not associated with increased cognitive load (verifications with and without checklists: 3.7 ± 1.9 and 3.3 ± 2.0, respectively; conditions with and without checklists: 4.0 ± 1.8 versus 3.9 ± 2.0, respectively [p = not significant for both]). Checklists resulted in greater interpretation times and lengths (p < 0.01 for all). However, checklists were efficient in terms of the cognitive load invested, interpretation time and interpretation length (p < 0.01 for all). CONCLUSIONS: Among ECG interpretation experts, checklist use during the verification stage of diagnostic decisions did not increase cognitive load or cause expertise reversal, but did reduce diagnostic error.
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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 | 0.027 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".