Bibliographic record
Abstract
BACKGROUND: The cause of the "treatment-risk paradox" reported for patients with coronary disease is unknown; however, determining the factors that contribute to this paradox is essential to properly design quality improvement interventions. METHODS: Prospective cohort study enrolling consecutive patients with angiographically proved coronary disease between February 1, 2004, and November 30, 2005, in Alberta. RESULTS: One month after an angiogram, statins were being taken by 2436 (62.9%) of 3871 patients (mean age, 64 years). High-risk patients were less likely to be taking statins than lower-risk patients (55.8% vs 63.5%; crude odds ratio [OR], 0.72 [95% confidence interval {CI}, 0.57-0.92]; risk ratio [RR], 0.88 [95% CI, 0.79-0.97]), but this treatment-risk paradox was completely attenuated by adjusting for exertional capacity and depressive symptoms (OR, 0.98 [95% CI, 0.75-1.28]; RR, 0.99 [95% CI, 0.89-1.09]). These results were robust across drug classes: while high-risk patients were less likely to be taking angiotensin-converting enzyme inhibitors, aspirin, and statins (25.8% vs 32.3%; crude OR, 0.73 [95% CI, 0.56-0.95]; RR, 0.80 [95% CI, 0.65-0.97]), this association did not persist in the adjusted model (OR, 0.98 [95% CI, 0.72-1.33] [P = .87]; RR, 0.99 [95% CI, 0.79-1.20]). CONCLUSIONS: The treatment-risk paradox reported in administrative database analyses is attributable to clinical factors not typically captured in these databases (such as functional capacity and depressive symptoms). Interventions to address the treatment-risk paradox should recognize that patients with reduced functional capacity, depression, or both are at higher risk for underuse of these beneficial therapies and should target physicians and patients.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".