The Association Between Complementary and Alternative Medicine and Health Status Following Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Complementary and alternative medicines (CAM) are commonly used in patients with cardiovascular disease. Although there is lack of evidence regarding the benefit of CAM on cardiovascular morbidity and mortality, health-status benefits could justify CAM use. HYPOTHESIS: Adoption of mind-body CAM after acute myocardial infarction (AMI) is associated with improved health status, though other forms of CAM are not associated with health-status improvement. METHODS: Patients with AMI from 24 US sites were assessed for CAM use (categorized as mind-body, biological, and manipulative therapies) prior to and 1 year after AMI. Among patients who reported not using CAM prior to their AMI, association of initiating CAM on patients' health status at 1 year after AMI was assessed using Angina Frequency and Quality of Life domains from the Seattle Angina Questionnaire and the Short Form-12 Physical and Mental Component scales. Multivariable regression helped examine association between use of different CAM therapies and health status. RESULTS: Among 1884 patients not using CAM at the time of their AMI, 33% reported initiating ≥1 forms of CAM therapy 1 year following AMI: 62% adopted mind-body therapies, 42% adopted biological therapies, and 15% began using manipulative therapies. In both unadjusted and adjusted analyses, we found no association between different types of CAM use and health-status improvement after AMI. CONCLUSIONS: There was no association between CAM use and health-status recovery after AMI. Until randomized trials suggest otherwise, these findings underscore the importance of focusing on therapies with proven effectiveness after AMI.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".