Optimal Medical Therapy for Non–ST-Segment–Elevation Acute Coronary Syndromes
Bibliographic record
Abstract
BACKGROUND: Acute coronary syndrome (ACS) patients in the highest risk categories are least likely to receive evidence-based treatments (EBTs). We sought to determine why physicians do not prescribe EBTs for patients with non-ST-segment-elevation ACSs and the factors determining use of these treatments after 1 year. METHODS AND RESULTS: One thousand nine hundred fifty-six non-ST-segment-elevation ACS patients were enrolled in the prospective, multicenter Canadian ACS registry II between October 2002 and December 2003. Each patient's physician gave reasons why guideline-indicated medication(s) was not prescribed during hospitalization. Medication use and reason(s) for discontinuation after 1 year were obtained by telephone interview of the patients. The commonest reason for not prescribing EBTs was "not high-enough risk" or "no evidence/guidelines to support use." However, Global Registry of Acute Coronary Events scores of patients not treated for this reason were often similar to or higher than those of patients prescribed such treatment. After 1 year, 77% of patients not on optimal ACS treatment at discharge remained without optimal treatment, and overall antiplatelet, β-blocker, and angiotensin-converting enzyme inhibitor use declined. Approximately one third of patients not taking EBTs had stopped their medication without instruction from their doctor. CONCLUSIONS: Nonprovision of EBTs may be due to subjective underestimation of patient risk and hence, likely treatment benefit. Oversights in care delivery were also apparent. Objective risk stratification, combined with efforts to ensure provision and adherence to EBTs, should be encouraged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".