Polyethylene Implants in Nasal Septal Restoration
Bibliographic record
Abstract
IMPORTANCE: Numerous techniques have been described to repair nasal septal perforations (SPs). However, many are technically challenging, with varying degrees of success. OBJECTIVE: To evaluate the use of polyethylene (Medpor; Porex Technologies) implants in the closure of nasal SPs. DESIGN AND SETTING: Prospective cohort study in an academic research setting. PARTICIPANTS: Fourteen patients with a nasal SP were identified between March 1, 2008, and February 1, 2011. INTERVENTION: Each patient underwent repair of the nasal SP with a polyethylene orbital sheet implant. After measuring the size of the SP, the implant was trimmed and shaped to fit appropriately. The implant was then placed between bilateral mucoperichondrial flaps using an endonasal approach. MAIN OUTCOME AND MEASURE: Successful closure of the nasal SP with an intact polyethylene graft and complete remucosalization by the 1-year follow-up visit. RESULTS: The most common initial symptoms of SPs were nasal obstruction, crusting, and epistaxis. The SPs ranged from 0.5 to 4.0 cm in diameter. Thirteen of 14 patients (93%) who underwent repair of their nasal SPs with a polyethylene implant had successful closure. CONCLUSION AND RELEVANCE: The use of polyethylene implants is effective and technically easy and is associated with low patient morbidity because it does not require the harvesting of tissue from other donor sites. LEVEL OF EVIDENCE: 4.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".