Individual Quality Improvement in Acute Coronary Syndromes
Bibliographic record
Abstract
Although treatment guidelines from the American College of Cardiology (ACC) and the American Heart Association (AHA) have been published and widely accepted, barriers to the optimal management of patients with acute coronary syndromes (ACS) still exist. Adherence to guidelines has been correlated with improvements in patient outcomes in ACS, including reduced mortality, yet data demonstrate that 25% of opportunities to provide guideline-recommended care are missed. This article describes a performance improvement (PI) initiative designed to address gaps in process-related ACS care and improve patient outcomes. PI is an American Medical Association-approved, standardized continuing medical education format in which physicians can earn up to 20 American Medical Association PRA category 1 credits by completing 2 phases of self-assessment and developing and implementing a PI plan to address self-identified areas in which patient care can be improved. In this ACS PI initiative, physicians will assess their practice using performance measures defined by the 2007 ACC/AHA ST-segment elevation myocardial infarction and unstable angina or non-ST-segment elevation myocardial infarction guideline updates within 3 general benchmark areas: (1) patient risk assessment, (2) initial pharmacologic management, and (3) time-to-treatment (ie, "door-to-needle," "door-to-balloon," and "door-in-door-out" times). After completing a self-assessment and identifying 1 or more areas of improvement, participants can complete educational interventions and access benchmark-specific tools that provide guidance on improving adherence with the ACC/AHA guidelines. This PI initiative supplements other ongoing quality improvement initiatives in ACS, but is unique in that it is the first to use individual physician self-assessment, benchmark-focused continuing medical education, and self-developed PI plans to improve process-related ACS care.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".