Bibliographic record
Abstract
We highly appreciate the interest expressed by colleagues Royse et al. in our recently published manuscript [1] and acknowledge their constructive remarks. Our study determined to evaluate long-term survival differences between multiple arterial grafting (MAG) versus single arterial grafting (SAG) in patients who underwent coronary artery bypass grafting [1]. At 12.6 years follow-up, MAG resulted in markedly lower all-cause death compared to an SAG strategy [23.6% vs 40.0%, respectively; adjusted hazard ratio 0.74, 95% confidence interval (0.55–0.98); P = 0.038]. Royse et al. hypothesized that the significant survival advantage of MAG could be reflected by the reduced number of venous grafts used in the MAG cohort (52.7%) versus the SAG cohort (98.4%). In the SAG cohort, 1.6% of patients (n = 16) underwent single-vessel coronary artery bypass grafting, which was performed with either a single left internal mammary artery graft or a single radial artery graft (table 2 in the main manuscript). Therefore, the MAG cohort was divided into 2 (unmatched) subgroups: (i) patients with total arterial revascularization (TAR, e.g. those without a venous graft) versus (ii) patients without TAR (e.g. those with a venous graft; No-TAR). At 12.6 years of follow-up, no difference in survival outcome was noted between TAR vs No-TAR by Kaplan–Meier analysis [all-cause death: 23.9% vs 23.0%, respectively; unadjusted hazard ratio 1.07, 95% confidence interval (0.72–1.58); P = 0.75]. Although previous publications have reported reduced patency in venous grafts compared to arterial grafts [2, 3], no difference in survival between groups was observed. Moreover, in the presence of durable arterial grafts to the left anterior descending artery, it remains unclear whether reduced patency rates in conduits to non-left anterior descending vessels translate to clinically meaningful survival differences. More data are needed to truly understand the possible detrimental impact of venous conduits in the presence of more than 1 arterial graft.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.058 | 0.036 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".