187 * QUICKER AND SAFER: SKELETONIZATION OF INTERNAL MAMMARY ARTERIES WITH HARMONIC TECHNOLOGY IN 910 PATIENTS WITH 1533 MAMMARY ARTERIES
Bibliographic record
Abstract
Objectives: Skeletonization of the internal mammary artery (IMA) reduces deep sternal infection but is more time-consuming and tedious than pedicle harvest. We wished to determine whether the use of harmonic technology facilitates skeletonization of the IMA. Methods: In a consecutive series of 1001 patients undergoing isolated coronary artery bypass graft (CABG) surgery from 2003 to 2012, adverse events and harvest times were compared between harmonic (910 patients) and non-harmonic patients (91 patients). Results: Harmonic technology was used to harvest 1533 mammary arteries in 910 (91%) of 1001 consecutive CABG patients; 74% of patients received bilateral IMA. Demographics of patients with and without harmonic skeletonization respectively were: age 64.6 vs 67.9 years, diabetes 35% vs 33%; women 22% vs 25%; and median EuroSCORE 2.9 vs 3.3. The mean harvest time for 78 non-harmonic skeletonized mammary arteries (50 surgeries) was 32.2 min (95% CI 30.1, 34.3), for harmonic skeletonized arteries after 450 surgeries was 25 min, (95% CI 24.0, 25.9), and in recent surgeries is less than 20 min. Major adverse events for patients with and without harmonic skeletonization respectively were: reoperation for bleeding 2.9% vs 0% (95% CI 3.9, 1.7); damaged mammary arteries 0.8% vs 0% (95% CI 1.3, 0.2); deep sternal infection 1.65% vs 1.1% (95% CI -2.8, +1.7); at a mean follow-up of 5 years, perioperative infarction 1.76% vs 2.2% (95% CI -2.7, +3.6), LIMA patency: 96.2% vs 100% (95% CI -0.4, +8.0). Conclusions: In this largest series to date of harmonic IMA skeletonization, this technique results in rare damage, is quicker, with comparable adverse events to the non-harmonic method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".