Rapid Change in Prescribing Behavior in Hospitals Participating in Get With The Guidelines–Stroke After Release of the Management of Atherothrombosis With Clopidogrel in High-Risk Patients (MATCH) Clinical Trial Results
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Physician prescribing patterns change slowly despite published randomized trials and consensus guidelines. We measure the effect of Management of Atherothrombosis With Clopidogrel in High-Risk Patients (MATCH) trial on discharge prescribing patterns for patients with stroke and those with transient ischemic attack in the Get With The Guidelines (GWTG)-Stroke Program. METHODS: We analyzed discharge prescribing patterns of antithrombotic medications for patients admitted with ischemic stroke or transient ischemic attack at hospitals participating in GWTG-Stroke between October 2002 to January 2006. Clinical information by quarter was analyzed in relation to publication of the MATCH study. Frequency of discharge prescription of aspirin+clopidogrel post-MATCH publication was compared with the pre-MATCH period after adjusting for patient and hospital characteristics and clustering by hospital. RESULTS: A total of 107 872 patients at 632 sites were eligible to receive antithrombotic therapy at discharge. Use of aspirin+clopidogrel therapy declined from 22.4% to 15.4% of patients after the publication of MATCH (adjusted OR 0.62, 95% CI 0.56 to 0.70, P<0.0001). Analysis by quarter revealed a rapid and sustained decrease in use of aspirin+clopidogrel therapy for the remainder of the study period. CONCLUSIONS: A rapid and sustained reduction in the frequency of aspirin+clopidogrel use in ischemic stroke and transient ischemic attack was observed after publication of the MATCH trial in the absence of MATCH-specific GWTG-Stroke initiatives and preceding an American Heart Association guideline update.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".