Consensus-Based Expert Development of Critical Items for Direct Observation of Point-of-Care Ultrasound Skills
Bibliographic record
Abstract
ABSTRACT Background Point-of-care ultrasound (POCUS) is increasingly used in a number of medical specialties. To support competency-based POCUS education, workplace-based assessments are essential. Objective We developed a consensus-based assessment tool for POCUS skills and determined which items are critical for competence. We then performed standards setting to set cut scores for the tool. Methods Using a modified Delphi technique, 25 experts voted on 32 items over 3 rounds between August and December 2016. Consensus was defined as agreement by at least 80% of the experts. Twelve experts then performed 3 rounds of a standards setting procedure in March 2017 to establish cut scores. Results Experts reached consensus for 31 items to include in the tool. Experts reached consensus that 16 of those items were critically important. A final cut score for the tool was established at 65.2% (SD 17.0%). Cut scores for critical items are significantly higher than those for noncritical items (76.5% ± SD 12.4% versus 53.1% ± SD 12.2%, P < .0001). Conclusions We reached consensus on a 31-item workplace-based assessment tool for identifying competence in POCUS. Of those items, 16 were considered critically important. Their importance is further supported by higher cut scores compared with noncritical items.
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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 | 0.223 | 0.320 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".