Multi-dimensional analysis of oral cavity and oropharyngeal defects following cancer extirpation surgery, a cadaveric study
Bibliographic record
Abstract
BACKGROUND: Defects following resection of tumors in the head and neck region are complex; more detailed and defect-specific reconstruction would likely result in better functional and cosmetic outcomes. The objectives of our study were: 1) to improve the understanding of the two- and three-dimensional nature of oral cavity and oropharyngeal defects following oncological resection and 2) to assess the geometric dimensions and the shapes of fasciocutaneous free flaps and locoregional tissue flaps required for reconstruction of these defects. METHODS: This study was an anatomic cadaveric study which involved creating defects in the oral cavity and oropharynx in two cadaveric specimens. Specifically, partial and total glossectomies, floor of mouth excisions, and base of tongue excisions were carried out. These subsites were subsequently geometrically analyzed and their volumes measured. The two-dimensional (2D) assessment of these three-dimensional (3D) structures included measures of surface area and assessment of tissue contours and shapes. RESULTS: The resected specimens all demonstrated unique dimensional geometry for the various anatomic sites. Using 2D analysis, hemiglossectomy defects revealed right triangle geometry, whereas total glossectomy geometry was a square. Finally, the base of tongue defects exhibited a trapezoid shape. CONCLUSIONS: Customizing the geometry and dimensions of fasciocutaneous free flaps so that they are specific to the confronted head and neck defects will likely result in better functional and cosmetic outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".