Skeletonized Internal Thoracic Artery Harvest Reduces Pain and Dysesthesia and Improves Sternal Perfusion After Coronary Artery Bypass Surgery
Bibliographic record
Abstract
BACKGROUND: Observational studies suggest that skeletonization of the internal thoracic artery (ITA) can improve conduit flow and length and reduce deep sternal infections and postoperative pain. We performed a randomized, double-blind, within-patient comparison of skeletonized and nonskeletonized ITAs in patients undergoing coronary surgery. METHODS AND RESULTS: Patients (n = 48) undergoing bilateral ITA harvest were randomized to receive 1 skeletonized and 1 nonskeletonized ITA. Intraoperatively, ITA flow was assessed directly and with a Doppler flow probe before and after topical application of papaverine. ITA harvest time and conduit length were recorded. A blinded assessment of pain (visual analog scale) and dysesthesia (physical examination) was performed at discharge, at 2 weeks, and at a 3-month follow-up. Sternal perfusion was assessed with nuclear imaging (n = 7). Skeletonization required longer ITA harvest times (27 +/- 1 versus 24 +/- 1 minutes; P = 0.04). There was a trend toward increased ITA length in the skeletonized group (18.2 +/- 0.3 versus 17.7 +/- 0.3 cm; P = 0.09). In situ ITA flow was lower in skeletonized arteries (7.4 +/- 0.9 versus 10.1 +/- 1.0 mL/min; P = 0.01) and increased significantly after ITA division and papaverine application. Postanastomotic flows were similar between groups. Skeletonization was associated with decreased pain at the 3-month follow-up and a reduction in major sensory deficits at the 4-week and 3-month (17% versus 50%; P = 0.002) follow-ups. Baseline adjusted sternal perfusion was significantly greater by 17 +/- 6% (P = 0.03) on the skeletonized side. CONCLUSIONS: Skeletonization results in reduced postoperative pain and dysesthesia and increased sternal perfusion at follow-up but does not produce increased conduit flow. ITA skeletonization may be a strategy for reducing morbidity after CABG.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".