The Relationship of Body Mass Index to Outcomes after Percutaneous Coronary Intervention
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effect of body mass index (BMI) on in-hospital outcomes in patients undergoing percutaneous coronary intervention (PCI) at a tertiary care hospital center in Ontario, Canada. BACKGROUND: Obesity is present in a large population of patients undergoing revascularization with PCI. METHODS: Retrospective analysis of 4,631 patients aged 62.0 +/- 12 years, stratified by BMI into five groups: nonobese (<25 kg/m2); overweight (25-29.9 kg/m2); class I obese (30-34.9 kg/m2); class II obese (35-39.9 kg/m2); and class III obese (> or =40 kg/m2). RESULTS: A BMI >25 kg/m2 was present in 79% of patients, and 35% were obese (BMI > or =30 kg/m2). Obese patients, particularly the class III obese, were significantly younger and had higher prevalence of diabetes, hypertension, and dyslipidemia (P < 0.0001). After adjustment for several covariates, lower BMI was independently associated with higher risk of major bleeding requiring transfusion (adjusted odds ratio [OR]= 1.40, 95% confidence interval [CI] 1.04-1.88, P = 0.025), and femoral hematoma (adjusted OR = 1.14, 95% CI 1.05-1.25, P = 0.003) in lean (<20 kg/m2) and normal BMI (20-24.9 kg/m2) patients. Obesity was not associated with death, myocardial infarction, repeat PCI, coronary artery bypass grafting, or major adverse cardiac event. CONCLUSIONS: Obesity is not associated with increased risk of adverse postprocedural in-hospital outcomes. These findings, however, do not discount the need for sustained efforts in secondary prevention of obesity and its consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".