Faculty Opinions recommendation of A new instrument to assess physician skill at thoracic ultrasound, including pleural effusion markup.
Bibliographic record
Abstract
BACKGROUND: To reduce complications and increase success, thoracic ultrasound is recommended to guide all chest drainage procedures. Despite this, no tools currently exist to assess proceduralist training or competence. This study aims to validate an instrument to assess physician skill at performing thoracic ultrasound, including effusion markup, and examine its validity.METHODS: We developed an 11-domain, 100-point assessment sheet in line with British Thoracic Society guidelines: the Ultrasound-Guided Thoracentesis Skills and Tasks Assessment Test (UGSTAT). The test was used to assess 22 participants (eight novices, seven intermediates, seven advanced) on two occasions while performing thoracic ultrasound on a pleural effusion phantom. Each test was scored by two blinded expert examiners. Validity was examined by assessing the ability of the test to stratify participants according to expected skill level (analysis of variance) and demonstrating test-retest and intertester reproducibility by comparison of repeated scores (mean difference [95% CI] and paired t test) and the intraclass correlation coefficient.RESULTS: Mean scores for the novice, intermediate, and advanced groups were 49.3, 73.0, and 91.5 respectively, which were all significantly different (P < .0001). There were no significant differences between repeated scores.CONCLUSIONS: Procedural training on mannequins prior to unsupervised performance on patients is rapidly becoming the standard in medical education. This study has validated the UGSTAT, which can now be used to determine the adequacy of thoracic ultrasound training prior to clinical practice. It is likely that its role could be extended to live patients, providing a way to document ongoing procedural competence. PMID: 23539145
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.159 | 0.081 |
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".