Use of Critical Items in Determining Point-of-Care Ultrasound Competence
Bibliographic record
Abstract
We previously developed a workplace-based tool for assessing point of care ultrasound (POCUS) skills and used a modified Delphi technique to identify critical items (those that learners must successfully complete to be considered competent). We performed a standard setting procedure to determine cut scores for the full tool and a focused critical item tool. This study compared ratings by 24 experts on the two checklists versus a global entrustability rating. All experts assessed three videos showing an actor performing a POCUS exam on a patient. The performances were designed to show a range of competences and one included potentially critical errors. Interrater reliability for the critical item tool was higher than for the full tool (intraclass correlation coefficient = 0.84 [95% confidence interval [CI] 0.42-0.99] vs. 0.78 [95% CI 0.25-0.99]). Agreement with global ratings of competence was higher for the critical item tool (κ = 0.71 [95% CI 0.55-0.88] vs 0.48 [95% CI 0.30-0.67]). Although sensitivity was higher for the full tool (85.4% [95% CI 72.2-93.9%] vs. 81.3% [95% CI 67.5-91.1%]), specificity was higher for the critical item tool (70.8% [95% CI 48.9-87.4%] vs. 29.2% [95% CI 12.6-51.1%]). We recommend the use of critical item checklists for the assessment of POCUS competence.
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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 | 0.071 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".