Impact of functional testing results on prescription patterns of anti-anginal medication after coronary artery bypass graft surgery: results from the ROSETTA-CABG Registry.
Bibliographic record
Abstract
BACKGROUND: Although coronary artery bypass graft surgery (CABG) is known to reduce angina, previous studies have suggested that anti-anginal medication use is not significantly reduced after CABG. However, it is unclear how functional testing results have an impact on anti-anginal medication prescription patterns. OBJECTIVES: To determine whether post-CABG functional testing results impact prescription patterns of anti-anginal medication during the 12 months after CABG. METHODS: The Routine versus Selective Exercise Treadmill Testing after Coronary Artery Bypass Graft Surgery (ROSETTA-CABG) Registry is a prospective, multicenter study. We examined anti-anginal medication use (beta blockers, calcium channel blockers and nitrates) at discharge and at 12 months post-CABG with respect to functional testing results among 392 patients. RESULTS: Among the 392 patients, 146 had at least one functional test over the 12-month follow up period. Among the 146 patients, 17% had positive functional tests, 69% had negative tests, and 14% had indeterminate tests. Both beta blocker and calcium channel blocker use did not increase from discharge to 12 months following a positive test (N = 25) (84% vs. 80%; p = NS; 16% vs. 16%; p = NS), while nitrate use increased seven-fold (4% vs. 28%; p = 0.03). However, following a negative test (N = 100), beta blocker use only decreased modestly (85% vs. 70%; p = 0.01), while both calcium channel blocker and nitrate use were unchanged (30% vs. 20%; p = NS; 4% vs. 6%; p = NS, respectively). Following an indeterminate test (N = 21), anti-anginal medication use was unchanged (p = NS for all 3 classes of medication). CONCLUSION: The use of anti-anginal medication is only modestly reduced after CABG, and functional testing results during the first year post-CABG do not have a strong impact on prescription patterns of anti-anginal agents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".