Smoking, Colchicine and Postoperative Outcomes in Thoracic Surgery: Post Hoc Analysis of the COP-AF Randomized Controlled Trial
Bibliographic record
Abstract
Background: To determine, among patients who underwent major noncardiac thoracic surgery, the association between smoking and perioperative atrial fibrillation (AF) and myocardial injury after noncardiac surgery (MINS), and whether the effect of colchicine use on these outcomes varied by smoking status. Methods: This study is a subgroup analysis of the Colchicine for the Prevention of Perioperative Atrial Fibrillation (COP-AF) randomized clinical trial. A total of 3209 participants who underwent major noncardiac thoracic surgery were randomized to receive colchicine, 0.5 mg twice daily, or identical placebo, for 10 days starting 2-4 hours before surgery. The co-primary outcomes were clinically significant perioperative AF and MINS during the 14-day follow-up. Results: , 0.08). Conclusions: Current smoking was associated with a small but increased risk of perioperative AF but not MINS after thoracic surgery. The effect of colchicine use on either outcome was not modified by smoking status. Clinical Trial Registration: NCT03310125.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".