Bibliographic record
Abstract
OBJECTIVE: To compare the intraoperative outcomes of superficial musculoaponeurotic system plication, imbrication, and deep-plane rhytidectomy techniques. METHODS: Thirty-two patients undergoing primary deep-plane rhytidectomy participated. Each hemiface in all patients was submitted sequentially to 3 progressively more extensive lifts, while other variables were standardized. Four major outcome measures were studied, including the extent of skin redundancy and the repositioning of soft tissues along the malar, mandibular, and cervical vectors of lift. The amount of skin excess was measured without tension from the free edge to a point over the intertragal incisure, along a plane overlying the jawline. Using a soft tissue caliper, repositioning was examined by measurement of preintervention and immediate postintervention distances from dependent points to fixed anthropometric reference points. RESULTS: The mean skin excesses were 10.4, 12.8, and 19.4 mm for the plication, imbrication, and deep-plane lifts, respectively. The greatest absolute soft tissue repositioning was noted along the jawline, with the least in the midface. Analysis revealed significant differences from baseline and between lift types for each of the studied techniques in each of the variables tested. CONCLUSION: These data support the use of the deep-plane rhytidectomy technique to achieve a superior intraoperative lift relative to comparator techniques.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".