Use of an error‐focused checklist to identify incompetence in lumbar puncture performances
Bibliographic record
Abstract
CONTEXT: Checklists are commonly used in the assessment of procedural competence. However, on most checklists, high scores are often unable to rule out incompetence as the commission of a few serious procedural errors typically results in only a minimal reduction in performance score. We hypothesised that checklists constructed based on procedural errors may be better at identifying incompetence. OBJECTIVES: This study sought to compare the efficacy of an error-focused checklist and a conventionally constructed checklist in identifying procedural incompetence. METHODS: We constructed a 15-item error-focused checklist for lumbar puncture (LP) based on input from 13 experts in four Canadian academic centres, using a modified Delphi approach, over three rounds of survey. Ratings of 18 video-recorded performances of LP on simulators using the error-focused tool were compared with ratings obtained using a published conventional 21-item checklist. Competence/incompetence decisions were based on global assessment. Diagnostic accuracy was estimated using the area under the curve (AUC) in receiver operating characteristic analyses. RESULTS: The accuracy of the conventional checklist in identifying incompetence was low (AUC 0.11, 95% confidence interval [CI] 0.00-0.28) in comparison with that of the error-focused checklist (AUC 0.85, 95% CI 0.67-1.00). The internal consistency of the error-focused checklist was lower than that of the conventional checklist (α = 0.35 and α = 0.79, respectively). The inter-rater reliability of both tools was high (conventional checklist: intraclass correlation coefficient [ICC] 0.99, 95% CI 0.98-1.00; error-focused checklist: ICC 0.92, 95% CI 0.68-0.98). CONCLUSIONS: Despite higher internal consistency and inter-rater reliability, the conventional checklist was less accurate at identifying procedural incompetence. For assessments in which it is important to identify procedural incompetence, we recommend the use of an error-focused checklist.
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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 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".