Cosmetic Rostral Nasal Reconstruction After Nasal Planum and Premaxilla Resection: Technique and Results in Two Dogs
Bibliographic record
Abstract
OBJECTIVE: To describe a novel reconstructive technique after nasal planum and premaxilla resection. STUDY DESIGN: Case report. ANIMALS: Dogs (n=2) with squamous cell carcinoma (SCC) of the nasal planum. METHODS: A 9-year-old neutered female Labrador retriever (dog 1) and an 11-year-old neutered male Golden retriever (dog 2) had resection of the nasal planum and premaxilla for treatment of locally invasive SCC. Reconstruction of a nasal planum facsimile was based on use of the nonhaired pigmented margins of bilateral labial mucocutaneous rotation-advancement flaps. RESULTS: Reconstruction of the premaxilla by construction of a nasal planum facsimile resulted in uncomplicated wound healing and improved cosmesis. There was no tumor recurrence at 1290 (dog 1) and 210 (dog 2) days after surgery. CONCLUSION: Reconstruction of a nasal planum facsimile was successfully performed without complications in 2 dogs with high owner satisfaction with cosmetic appearance. CLINICAL RELEVANCE: This technique represents a significant advancement in surgical cosmetic outcome, may potentially reduce postoperative complications, and should be considered for dogs requiring nasal reconstruction after nasal planum resection with premaxillectomy.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".