The Table Tilt: Preventing Traction on the Brachial Plexus During Facelift Surgery
Bibliographic record
Abstract
Rhytidectomy remains a popular cosmetic surgical procedure, with recognized risks and complications.1 Surgeons who regularly perform facelifts continually seek out methods to improve their outcomes, which includes minimizing complications. We recently noticed a troublesome sequela not previously described: patients’ complaining of a continuous, deep, burning pain in the area of the anterior elbow joint. This pain was unilateral or bilateral and appeared refractory to intraoperative cushioning, massage, or change of arm position. We postulated that these symptoms2 were caused by traction on the brachial plexus in the neck due to prolonged rotation of the cervical spine resulting from the patient being positioned in a way that maximized visibility of the neck area for the surgeon during rhytidectomy procedures. Tilting the operating table approximately 15° away from the proposed surgical site resolved both issues (the visibility and resultant patient pain), providing a simple solution. The recommended table position is illustrated in the Figure. The operating table is tilted 15° away from the surgeon. The arrow points to the direction of the surgeon’s view. The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article. The authors received no financial support for the research, authorship, and publication of this article.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.004 |
| 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".