Nasal Reconstruction after Malignant Tumor Resection: An Algorithm for Treatment
Bibliographic record
Abstract
BACKGROUND: Seventy-five percent of nonmelanoma skin cancers are located in the head and neck area, of which 30 percent occur on the nose (225,000 new cases per year). The aim of this study was to develop a nasal reconstruction algorithm for nasal defects, based on experience with 788 consecutive nasal reconstructions performed in a multidisciplinary university medical center setting over a period of 7 years. METHODS: Medical files of 788 consecutive patients who were operated on for various nasal pathologies between January of 2001 and December of 2008 were reviewed. In addition, a literature search on treatment of nasal defects and outcomes after nasal reconstruction was conducted using PubMed. RESULTS: The algorithm divides nasal defects into simple, small (skin only), larger (skin and cartilage), or full thickness. Small defects can be closed primarily or with various local flaps. For larger defects, the three-stage paramedian forehead flap is the flap of choice with or without the use of cartilage grafts. For small inner lining defects, full-thickness skin grafts or turn-down lining flaps with delayed primary cartilage grafts at the intermediate stage are currently the authors' preference. For medium to larger inner lining defects, the folded forehead flap with delayed primary cartilage grafts at the intermediate stage is the authors' preferred technique. For (sub)total nasal reconstructions with very large inner lining requirements, the authors would now consider free vascularized tissue transfer. CONCLUSIONS: Nasal skin cancer is an increasing problem. Proper treatment of nasal skin cancer, including nasal reconstruction, requires a structured multidisciplinary approach to achieve excellent tumor control and a satisfactory aesthetic and functional end result.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".