Abstract 15576: Implementation of a Knowledge Translation and Education Tool to Improve Appropriate Use of Stress Echocardiography at a Large Academic Medical Center in a Public Funded Healthcare System
Bibliographic record
Abstract
Introduction: Previous retrospective studies have suggested that Appropriate Use of Stress Echocardiograms (SE) is sub-optimal, but there have not been effective interventions developed to improve appropriate use of SE. Methods: We conducted a prospective, pre (Jul 1 - Oct 15 2013) and post (Mar 1-May 31 2014) time series analysis of an educational intervention that included the development and implementation of a new ordering requisition that integrated Appropriate Use Criteria (AUC) for SE within it. Results: During the control period, 221 consecutive were evaluated using the 2011 AUC and 98% were classifiable. Overall the inappropriate rate of classifiable studies was 32%, while the appropriate rate was 64% and uncertain rate 4%. During the intervention period, 156 studies were evaluated and 98% were classifiable. Implementation of the KT tool and educational intervention resulted in an increase in the appropriateness rate to 76% (p=0.016) and a reduction in the inappropriate rate to 19% (p=0.003) of a...
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".