Modification of the Superficial Cervical Axial Pattern Skin Flap for Oral Reconstruction
Bibliographic record
Abstract
OBJECTIVE: To describe an extended pedicle flap based on the superficial cervical artery (SCA) for closure of oral defects in dogs. STUDY DESIGN: Anatomic study; in vivo experimental study. ANIMALS: Canine cadavers (13) and 3 dogs. METHODS: The prescapular branch of the SCA was cannulated and perfused with a lead oxide gelatin mixture. The area perfused by 1 SCA was examined as was the rostral extent of the flap. Staged implantation was performed to evaluate flap performance in vivo. In stage 1, the flap was prepared for implantation into the oral cavity. In stage 2, the flap was fully developed to include the 1 degrees, 2 degrees, and partial 3 degrees angiosome of 1 SCA pedicle. The flap was transposed by a bridging incision and a parapharyngeal tunnel into the oral cavity. The flap was used to reconstruct a partial-thickness defect created in the palate. RESULTS: The territory of the contralateral SCA was captured in all cadavers. The full flap reached the level of the canine teeth in all cadavers. In live dogs, necrosis was not observed after implantation into partial-thickness defects and dehiscence was minimal. Loss of pliability secondary to de-epithelialization and staging resulted in a limitation of rostral reach of the flap. CONCLUSIONS: Whereas the flaps did not reach as far rostrally as anticipated, they survived well in the harsh oral environment. The flap may be modified to reconstruct full-thickness palatal defects. CLINICAL RELEVANCE: The extended SCA pattern flap may be adapted for closure of oral defects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".