Patient Satisfaction With Expanded Polytetrafluoroethylene (Softform) Implants to the Perioral Region
Bibliographic record
Abstract
OBJECTIVE: To assess clinical results in patients undergoing implantation of expanded polytetrafluoroethylene (Softform) for perioral enhancement (melolabial fold, melomental fold, upper lip, and lower lip). DESIGN: Fifty patients had undergone Softform implantation by a single surgeon. A retrospective telephone survey (25 questions) was conducted. Of 50 patients, 38 (76%) were contacted. The mean interval between the procedure and survey was 22.7 months (range, 2-40 months). Responses were submitted for statistical analysis. A pathological review was performed on specimens removed from 2 patients. RESULTS: Two patients (4%) developed postoperative infections that resolved with use of oral antibiotics; 5 patients (10%) requested repositioning owing to dissatisfaction with placement; and 5 patients (10%) requested implant removal. Composite scores indicated that patients were "slightly" satisfied with the procedure outcome. Of the 38 patients contacted, 24 (63%) would undergo additional implants and 20 (53%) would recommend the procedure to others. Results were not significantly influenced by site, size, or history of prior augmentation procedures. Histologic review indicated that implants elicit a chronic inflammatory reaction and that blood vessels infiltrate the porous walls of the implant. CONCLUSION: With proper patient selection, Softform represents a potential option for those individuals considering perioral enhancement.
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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 | 0.006 |
| 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) | 0.004 | 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".