Beneficial effects of carvedilol as a concomitant therapy to angiotensin-converting enzyme inhibitor in patients with ischemic left ventricular systolic dysfunction
Bibliographic record
Abstract
Studies are scant on the effects of short-term carvedilol treatment as an adjuvant to angiotensin-converting enzyme (ACE) inhibitor in patients with left ventricular (LV) systolic dysfunction. The objective of this study was to find the effects of short-term treatment of carvedilol on patients with ischemic LV systolic dysfunction (defined as LV ejection fraction (LVEF) ≤30% on 2D echocardiography) undergoing coronary artery bypass surgery (CABG). There were 74 patients that received ACE inhibitor without any β-blocker (control) and 67 patients that received carvedilol in addition to ACE inhibitor following CABG (carvedilol group). After 1 month of drug administration following CABG, the control group was found to have significantly greater percent improvement in LVEF (29.1% ± 5.39%) as compared with the carvedilol group (15.3% ± 4.89%). However, after 3 and 6 months, LVEF levels were found to be significantly greater in the carvedilol group as compared with the control group. Further, at 6 months of drug administration, LV end systolic diameter was significantly less in the carvedilol group (39.11 ± 1.10 mm) as compared with the control group (43.49 ± 1.39 mm). Thus, carvedilol produces beneficial effect on short-term administration in terms of LV contractility when given along with ACE inhibtior as compared with ACE inhibitor therapy alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 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".