Nasal Reconstruction after Malignant Tumor Resection: An Algorithm for Treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it