Complex Nasal Reconstruction in a Wide-awake Ambulatory Setting: A Study of Efficacy and Perioperative Patient Experience
Bibliographic record
Abstract
The use of local anesthesia in nasal surgery has gained popularity as a cost-effective and safe alternative. With the potential benefit of reconstruction without using general anesthesia, the goal of the study was to evaluate the patient-reported experience in addition to surgical outcomes and perioperative complications. Methods: A mixed-methods study was completed with retrospective chart review and patient-reported outcome questionnaire. The primary outcome measures were demographics, oncologic and surgical details, and postoperative complications. Secondary outcome measures were aesthetic outcomes and procedure tolerance, which were gathered from the FACE-Q questionnaire. Results: Of the 22 patients who met inclusion criteria, nine patients (41%) had forehead flaps performed and 13 patients (59%) had multilayer reconstruction with local flaps and cartilage grafts. The average number of surgeries performed, including revisions, was 2.3 ± 0.2. The overall complication rate for reconstructions and revisions was 20%, most of these were minor complications. The overall subjective rating of patient's appearance was excellent with an average score of 83.9 (± 17.3) out of 100. There was minimal appearance distress as a product of the surgery with an average score of 84.9 (±18.8). On a scale of one to five, patients reported an average of 4.3 for convenience, 3.8 for efficiency of setup and procedure, and 3.4 out of 5 for comfortability with having an operation performed on their face wide awake. Conclusion: Our study demonstrated that complex nasal reconstruction performed under local anesthetic in a minor procedure setting is a feasible and safe option with good patient-reported outcomes.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".