Transnasal Endoscopic Medial Maxillectomy for Inverting Papilloma
Bibliographic record
Abstract
OBJECTIVE: To describe the new technique of transnasal endoscopic medial maxillectomy. STUDY DESIGN: Study design included application of the new technique in the management of five patients with inverting papilloma; retrospective review of five patients who had lateral rhinotomy with medial maxillectomy for inverting papilloma; comparison of transnasal endoscopic medial maxillectomy to open medial maxillectomy for scope of resection, margin control, operative time, and surgical access; and detailed description of transnasal endoscopic medial maxillectomy. METHODS: Charts were reviewed and tabulated for operative time, duration of follow-up, and recurrence. Pathology reports were reviewed for number and orientation of the specimens and for margin control. RESULTS: Operative time was shorter for patients managed with transnasal endoscopic medial maxillectomy. All patients with transnasal endoscopic medial maxillectomy had one large, well-oriented specimen with margin control. There was no recurrence in either group. CONCLUSIONS: Transnasal endoscopic medial maxillectomy providing full access to the maxillary and ethmoid sinuses is described in detail. Transnasal endoscopic medial maxillectomy is an effective, reproducible technique with less operative time and morbidity and, possibly, better pathological tumor mapping than open medial maxillectomy for selected patients. Maxillary sinus involvement with inverting papilloma is not a contraindication for this technique. Strong illumination, superior resolution, and angled visualization, coupled with exact osteotomies, make transnasal endoscopic medial maxillectomy an efficacious technique for inverting papilloma with extension limited to the maxillary and ethmoid sinuses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".