Surgical myocardial revascularization using the left internal thoracic artery in patients with diabetes mellitus
Bibliographic record
Abstract
Objective:to evaluate the immediate and somewhat distant results of surgical revascularization using the left MKA in patients with diabetes, compared with the results of autovenous CABG, to identify possible complications when using the left MA in patients with diabetes.Materials and methods: 2 groups of patients who, from 2010 to 2012, were selected. performed artery bypass surgery. All patients had type II diabetes. In the first group, the mammaro-coronary artery bypass surgery (MBS) was always used, in the second group and was not performed for various reasons.Results:evaluated indicators aft er 1 year and 6 years. In the immediate postoperative period, we noted a decrease in the class of angina in both groups. We did not observe a significant difference in the violation of the healing of the sternum. In the long-term period, in the group where MBS was performed, we noted a lower mortality rate, a lower class of angina pectoris and a smaller percentage of complications in the cardiovascular system.Conclusions:In patients with multifocal lesions of the coronary bed and concomitant diabetes, the preferred method of coronary artery bypass surgery is MBS, which can be supplemented with CABG. This is confirmed by sixyear observation. MKA can be safely used in diabetes and especially in the stem lesion of the left lance. Problems with the healing of the sternum with careful allocation of LMA we have not noted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".