New Frontiers in Wide-Awake Surgery
Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Explain the most important benefits of wide-awake surgery to patients. 2. Tumesce large parts of the body with minimal pain local anesthesia injection technique to eliminate the need for sedation for many operations. 3. Apply tourniquet-free surgery to upper and lower limb operations to avoid the sedation required to tolerate tourniquet pain. 4. Move many procedures out of the main operating room to minor procedure rooms with no increase in infection rates to decrease unnecessary cost and solid waste in surgery. SUMMARY: Three disruptive innovations are changing the landscape of surgery: (1) minimally painful injection of large-volume, low-concentration tumescent local anesthesia eliminates the need for sedation for many procedures over the entire body; (2) epinephrine vasoconstriction in tumescent local anesthesia is a good alternative to the tourniquet and proximal nerve blocks in extremity surgery (sedation for tourniquet pain is no longer required for many procedures); and (3) evidence-based sterility and the elimination of sedation enable many larger procedures to move out of the main operating room into minor procedure rooms with no increase in infection rates. This continuing medical education article explores some of the new frontiers in which these changes affect surgery all over the body.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".