Quantifying Clinically Meaningful Point-of-Care Ultrasound Interpretation Discrepancies Using an Emergency Department Quality Assurance Program
Bibliographic record
Abstract
OBJECTIVES: Emergency medicine professional associations recommend that quality assurance (QA) programs be implemented wherever emergency department (ED) point-of-care ultrasound (POCUS) is in use. The purpose of this study is to identify the rate of clinically meaningful interpretation discrepancies between initial ED POCUS interpretation and a gold standard using a QA program in a Canadian academic ED. METHODS: All POCUS examinations completed in our ED are subject to a QA process. The results of all POCUS examinations undergoing this process from July 1, 2014, to June 30, 2015, were retrospectively reviewed. Four blinded abstractors collected data with a standardized tool after a training session. Information regarding patient demographics, POCUS indication, emergency physician initial POCUS interpretation, physician POCUS expertise, the presence of an interpretation discrepancy, and whether the discrepancy was clinically meaningful was abstracted. The proportion of interpretation discrepancies, clinically meaningful discrepancies, discrepancies requiring remedial action, and differences in discrepancy rates between non-expert and expert sonographers were analyzed. RESULTS: A total of 2,869 POCUS studies were included for review, with 2,668 in the final data set after exclusions. In total, only 1.4% of all scans contained an interpretation discrepancy. The rate of clinically meaningful discrepancies was 0.5%, and the rate of scans requiring remedial action was 0.1%. Overall, 85.5% of all scans were performed by four POCUS expert physicians, with the remainder by a non-expert. Scans performed by non-expert sonographers were significantly more prone to discrepancies than those performed by experts. No single scan indication was more prone to discrepancy. CONCLUSIONS: The overall ED POCUS interpretation discrepancy rate and clinically meaningful discrepancy rate identified using our QA process were very low. The findings are limited by the small group of expert sonographers completing most scans.
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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 | 0.085 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".