Selective ultrasound guided pectoral nerve targeting in breast augmentation: How to spare the brachial plexus cords?
Bibliographic record
Abstract
Subpectoral breast augmentation surgery under regional anesthesia requires the selective neural blockade of the medial and lateral pectoral nerves to diminish postoperative pain syndromes. The purpose of this cadaver study is to demonstrate a reliable ultrasound guided approach to selectively target the pectoral nerves and their branches while sparing the brachial plexus cords. After evaluating the position and appearance of the pectoral nerves in 25 cadavers (50 sides), a portable ultrasound machine was used to guide the injection of 10 ml of 0.2% aqueous methylene blue solution in the pectoral region on both sides of three Thiel's embalmed cadavers using a single entry point-triple injection technique. This technique uses a medial to lateral approach with the entry point just medial to the pectoral minor muscle and three subsequent infiltrations: (1) deep lateral part of the pectoralis minor muscle, (2) between the pectoralis minor and major muscles, and (3) between the pectoralis major muscle and its posterior fascia under ultrasound visualization. Dissection demonstrates that the medial and lateral pectoral nerves were well stained while leaving the brachial plexus cords unstained. We show that 10 ml of an injected solution is sufficient to stain all the medial and lateral pectoral nerve branches without a proximal extension to the cords of the brachial plexus.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".