P5224Improving the Appropriate Use of Transthoracic Echocardiography- The results of the Echo WISELY trial
Bibliographic record
Abstract
Background: Appropriate use criteria (AUC) have defined rarely appropriate (rA) as transthoracic echocardiograms (TTEs) for which there is a clear lack of benefit. Single center studies have shown AUC–based educational initiatives reduce rA TTEs, however, it remains unknown whether such initiatives are effective in improving TTE ordering across multiple clinical settings. This study sought to investigate the impact of an AUC–based educational intervention on outpatient TTE ordering in an international multi-centered study of cardiologists and primary care providers. Methods: We conducted a prospective, investigator blinded, multi-centered, randomized controlled trial of an AUC-based educational intervention aimed to reduce rA outpatient TTEs. The study was conducted at eight hospitals across two countries. We randomized cardiologists and primary care providers to receive either intervention (educational online lecture on AUC, access to the American Society of Echocardiography Echo AUC App on appropriateness, and monthly individualized physician feedback on ordering behavior via email) or control (no intervention). The primary outcome measure was the proportion of rA TTEs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".