Blood transfusion and risk of atrial fibrillation after coronary artery bypass graft surgery
Bibliographic record
Abstract
The aim of this study was to systematically evaluate the effect of blood transfusion (BT) on postoperative atrial fibrillation (AF) in adult patients who had undergone coronary artery bypass grafting (CABG) surgery.PubMed, Embase, and Cochrane Library databases from inception to January 2017 were searched. Cohort studies were searched that evaluated the association between BT and the risk of postoperative AF in adult patients who had undergone CABG surgery. Study quality was assessed by using the Newcastle-Ottawa scale (NOS). A meta-analysis was performed with the random-effect model.Eight cohort studies involving 7401 AF cases and 31,069 participants were identified and included in our data analysis. The pooled odds ratio of postoperative AF in patients with BT was 1.45 (95% confidence interval, 1.26-1.67), with significant heterogeneity (P < .0001, I = 79%). Excluding one study that had an off-pump CABG did not significantly impact this result (odds ratio, 1.36; 95% confidence interval, 1.23-1.50; n = 7). To examine the stability of the primary results, we performed subgroup analyses. The association between BT and the risk of postoperative AF was similar, as determined in the stratified analyses conducted according to study design, type of surgery, and country.The findings of the present meta-analysis demonstrated a statistically significant increase in postoperative AF risk among adult patients with BT. Further prospective large-scale studies are needed to establish causality and to elucidate the underlying mechanisms.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".