International Interdisciplinary Rhytidectomy Survey
Bibliographic record
Abstract
Facial rhytidectomy is a complex and multi-faceted operation performed by different methodologies between practitioners. This study elucidates current international trends in facelift surgery, including patient selection, operative technique, and postoperative care. A 43-item questionnaire was sent electronically to 7247 members of the following societies: ASPS, ISAPS, CSPS, IFFPS, and the AAFPRS. The survey focused on 3 main areas: (a) background information, (b) intraoperative technique, and (c) postoperative care. The response rate was 11.4%. The majority of our population was from the United States (US) (73%). Most (85%) of the respondents have practices where over 50% of their procedures are considered aesthetic surgery. Statistical differences between the uses of minimally invasive adjuvant treatments (thread lifts, endotine mid-face devices, superficial and deep skin resurfacing procedures) were found between plastic surgeons (PS) and facial plastic surgeons (FPS), as well as between US, Canadian, and international surgeons. Suture imbrication (42%) was the most common way of handling the submuscular aponeurotic system. International surgeons were more likely (49.6% vs. 37.7%, P < 0.05) to use this technique than US or Canadian surgeons. Difference in handling patients who smoke and postoperative management differences were also found between the groups queried. No differences were found between FPS and PS in the handling of the submuscular aponeurotic system, treatment of platysmal bands, treatment of ptotic submandibular glands, or treatment of submental fat deposits (P > 0.05). Differences exist between FPS and PS, and between US, Canadian, and international surgeons with regard to facelift techniques and perioperative management. These differences need to be addressed in order to measure outcomes across specialties and between techniques. This data will additionally be helpful for less experienced and younger surgeons who wish to define best practice patterns.
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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 | 0.001 | 0.007 |
| 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 | 0.000 | 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 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".