Therapeutic Management in Patients with Renal Failure who Experience an Acute Coronary Syndrome
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Prior reports have suggested that patients with impaired renal function receive less aggressive care after an acute coronary syndrome (ACS). The aim of this study was to determine whether this held true in a contemporary cohort, after thorough adjustment for cotreatments/comorbidities. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Patients who were admitted for an ACS in eight participating hospitals were stratified into three groups according to estimated creatinine clearance (CrC): less than 45 ml/min, 45 to 60 ml/min, and reference >60 ml/min. RESULTS: During hospitalization, uses of reperfusion therapy in tertiary care centers [difference between CrC < or =45 ml/min and reference group (Delta): 4%, 95% confidence interval (CI): (-13%, 21%)] and systemic anticoagulation [Delta: 0%, CI (-5%, 5%)] were similar in the three groups. Coronary angiography was performed less often in patients with lower CrC [Delta: -16%, CI: (-31%, -1%)]. At discharge, nearly all patients received either an antiplatelet agent or warfarin regardless of CrC [Delta: -1%, CI: (-3%, 1%)]. Discharge use of angiotensin converting enzyme (ACE) inhibitors or angiotensin-receptor blockers was comparable [Delta: 7%, CI: (-1%, 15%)]. beta-blockers [Delta: -9%, CI: (-17%, -1%)] and lipid-lowering drugs (LLDs) [Delta: -7%, CI: (-13%, -1%)] were used less frequently in patients with lower CrC. In multivariate analyses, decreased CrC predicted lower coronary angiography and LLD use, but not lower beta-blocker use at discharge. CONCLUSIONS: These results suggest that in patients with ACS, the extent of undertreatment due to chronic kidney disease is less than reported previously, which is partially explained by more complete adjustment for cotreatments/comorbidities.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".