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 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 | 0.002 | 0.016 |
| 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".