Nasal Septal Perforation Reconstruction with Polydioxanone Plate: A Systematic Review
Bibliographic record
Abstract
Nasal septal perforation is an uncommon pathology that is difficult to surgically repair and may significantly impact patients' quality of life. Existing treatments have high complication and failure rates. The use of polydioxanone (PDS) plates to repair septal perforations is an innovative approach that has demonstrated superior outcomes to the conventional techniques. This study aimed to review the literature on PDS plates for nasal septal perforation reconstruction. PubMed, OVID Medline, and OVID Embase databases were searched for relevant articles in June 2021. Search terms included nasal septal perforation, polydioxanone, septal perforation, septal repair, nasal septum, and PDS plate. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were adhered to for this systematic review. Database searches yielded 80 articles. Seven articles were included representing 74 patients. All studies reported the use of PDS plates in addition to other materials. They all reported closure rates of at least 80%. The majority of studies reported no postoperative complications. Nasal septal perforation reconstruction with PDS plates is a promising approach that has demonstrated positive outcomes. Further larger studies are required to evaluate the long-term efficacy of using PDS plates on patients with septal perforations.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".