Lessons learned from the use of 1,977 in-situ bilateral internal mammary arteries: a retrospective study
Bibliographic record
Abstract
BACKGROUND: We sought to determine the early and long-term results of in-situ bilateral internal mammary artery (BIMA) grafting in patients undergoing coronary artery bypass graft surgery (CABG). METHODS: Between 1992 and 2011, 16,364 patients underwent primary isolated CABG involving at least one in-situ IMA at our institution. Among these, 1,977 patients underwent in-situ BIMA grafting: the right IMA was used to revascularize the right coronary artery system in 1,279, the circumflex system in 454 patients, and the left anterior descending (LAD) in 244. Logistic and Cox regression analyses were used to predict in-hospital mortality and cumulative late death. RESULTS: Late survival among BIMA patients was negatively and independently influenced by chronic obstructive pulmonary disease (hazard ratio (HR) 2.4, 95% confidence interval (CI) 1.6-3.4, p = 0.0005), age (HR 1.2, 95% CI 1.1-1.3, p < 0.001), and mediastinitis (HR 2.1, 95% CI 1.1-4.2, p < 0.03). Gender, body mass index, diabetes, choice of target for the second (non-LAD) IMA, and conduit grafted to the LAD (RIMA vs. LIMA) did not influence late survival among BIMA patients. A BIMA grafting strategy was significantly beneficial for younger patients. However, it was not associated with superior late survival for patients aged 66 years and above at the time of CABG, and showed a trend to harm among octogenarians (HR 1.05, 95% CI 0.70-1.56, p = 0.80). CONCLUSIONS: Female gender, non-insulin dependent diabetes, and the site of second IMA anastomosis did not influence early and long-term outcomes in patients undergoing CABG with in-situ BIMA grafting. The right and left IMAs are equally effective conduits for the LAD. However, advanced age, chronic obstructive pulmonary disease, and insulin-treated diabetes mellitus have a negative impact on late survival among patients with BIMA grafts.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| 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".