Representation of obesity in contemporary atrial fibrillation ablation randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: The prevalence and impact of obesity on outcomes of atrial fibrillation (AF) ablation randomized controlled trials (RCTs) have not been well studied. OBJECTIVE: To examine the proportion of participants with obesity enrolled in RCTs of AF ablation and outcomes of ablation when subgroup analysis of participants with obesity were available. METHODS: We systematically searched PubMed and EMBASE for AF ablation RCTs published between January 1, 2015 to May 31, 2022. When body mass index (BMI) data were available, normal distribution was assumed and a z score was used to estimate the proportion of obesity. Results categorized by BMI or body weight status were reviewed. Authors were contacted for additional information. RESULTS: Of 148 eligible RCTs with 30174 participants, 144 (97.30%) RCTs did not report the proportion of participants with obesity, while published information regarding BMI was available in 63.51%. Three trials excluded patients based on BMI. Using reported BMI, we estimated the proportion of participants with obesity varied greatly across these trials, ranging from 5.82%-71.9% (median 38.02%, interquartile 29.64%, 49.10%). Patients with obesity were represented in a greater proportion among trials conducted in North America (50.23%) and Asia (44.72%), compared to others (32.16%), p < .001. Subgroup analysis or analysis adjusting for BMI was reported in only 13 (8.78%) RCTs; four (30.77%) of these suggested that BMI or body weight might negatively affect primary outcomes. CONCLUSION: Obesity is a common comorbidity among AF patients. However, most AF ablation RCTs underreported the proportion of participants with obesity and its impact on the primary outcomes.
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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 | 0.434 | 0.751 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".