Applications of GORE-TEX Implants in Rhinoplasty Reexamined After 17 Years
Bibliographic record
Abstract
OBJECTIVE: To determine the efficacy of GORE-TEX (W. L. Gore & Associates Inc, Flagstaff, Arizona) alloplast in rhinoplasty. DESIGN: A 17-year retrospective medical chart review at a teaching hospital, community hospital, and private facial cosmetic surgery center. A total of 521 patients (122 male and 399 female; age range, 13-70 years) were followed for 12 months to 17 years. All patients had undergone GORE-TEX implantation rhinoplasty (685 implants in 158 primary procedures and 508 secondary procedures) performed by 1 surgeon. Patient satisfaction, expressed with respect to desired cosmetic benefit and functional outcome, and physician assessment, based on aesthetic improvement, technical considerations, and complications, were evaluated. Results were assessed according to the follow-up notes in the medical chart reflecting patients' and surgeon's comments and full preoperative and postoperative photographic documentation. RESULTS: GORE-TEX alloplasts, 1 to 10 mm thick, implanted in the nasal dorsum (n = 264), lateral nasal wall (n = 252), supratip dorsum (n = 85), and premaxilla (n = 84) showed excellent stability and tissue tolerance. Biological complications that required implant removal occurred in 1.9% of patients and included infection, soft tissue swelling, migration, and extrusion. CONCLUSIONS: With the exception of the nasal tip, columella, or problems in which corrections would require rigidity of the grafted or implanted material, the GORE-TEX alloplast is a safe, inexpensive, and predictable alternative to autografts. In the present series, more than 95% of implants used were 1 to 4 mm thick. In the remaining 5%, 6 implants ranged from 8 to 10 mm thick, and we found them acceptable. It is our opinion that for both primary and secondary rhinoplasty with adequate endonasal and external soft tissue coverage, GORE-TEX should be strongly considered for major and minor corrections of the nasal wall and bridge in properly selected patients.
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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 | 0.002 |
| 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 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".