Skeletonization of the internal thoracic artery for coronary artery bypass grafting
Bibliographic record
Abstract
PURPOSE OF REVIEW: Multiple cohort studies have shown that long-term outcomes after coronary surgery can be markedly improved when bilateral internal thoracic arteries (BITA) are used for revascularization. Applying this strategy is challenging, however, as a large proportion of patients suffer from comorbidities such as diabetes, and sternal devascularization with BITA may increase the risk of complications. Skeletonization is a technique of internal thoracic artery (ITA) harvest that has been proposed to reduce chest wall ischemia and thus minimize these problems. RECENT FINDINGS: Evidence is presented from animal and human studies which confirm that skeletonization preserves sternal blood flow. When meticulously completed, skeletonization is not associated with vessel wall damage. Intrapatient comparisons have conclusively demonstrated that skeletonization minimizes chest wall pain and discomfort. Many studies have confirmed that, when this strategy is used for BITA, the incidence of sternal wound infection approximates that seen with single ITA use, even in diabetic patients on insulin. Finally, there is no evidence in the current literature to suggest that skeletonization jeopardizes ITA patency. SUMMARY: The strategy of skeletonization is an advance that will facilitate complete arterial revascularization in high-risk patients whose surgery has previously been limited to saphenous vein conduits. Theoretical concerns challenging this approach should be tested long-term in a randomized clinical trial in patients undergoing BITA revascularization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.006 | 0.002 |
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".