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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".