Improved Veteran Outcomes Diagnosed with Myocardial Infarction
Bibliographic record
Abstract
Kentucky’s veteran population is approximately 295,390 with most veterans being sixty-five years of age or older (National Center for Veterans Analysis and Statistic, 2018). Due to Kentucky’s veteran population being sixty-five years and older, and since the leading cause of death in Kentucky is heart disease, tracking quality measures for coronary artery disease benefits the veteran population. The purpose of this scholarly project was to perform a chart review for every patient within the inpatient hospital that has had an ST-elevation myocardial infarction and non-ST elevated myocardial infarction, identified key areas of improvement, and implemented evidence-based American Heart Association's Get with the Guidelines CAD protocol to improve outcomes. Data was collected from charts with a diagnosis of STEMI and NSTEMI for the 2021 year. Two areas of focus were found for improvement. ECG within 10 minutes of arrival and cardiac rehabilitation orders from the inpatient setting was under expected benchmarks. Re-education of Emergency Department staff on identifying which patients to bring back for immediate ECG took place. Identification of which cardiology fellows are responsible for placing the cardiac rehab order and educated fellows on this need. Quarterly meetings among key stakeholders discussed patient data documenting implementation of the Get with the Guidelines CAD protocol and evaluated for benchmark achievements and implementation change needs. Quarter four data showed all benchmarks were met for a consecutive 90 days earning this inpatient hospital, the Mission: Lifeline NSTEMI Bronze award for 2021.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".