Transorbital Neuroendoscopy Surgery for the Management of Frontal Sinus Lesions: Case Series
Bibliographic record
Abstract
Introduction: Endoscopic transnasal surgery has provided an effective and safe approach for managing most frontal sinus lesions. Nevertheless, endonasal techniques may be insufficient in significantly large lesions or defects located lateral to the mid-orbital point or superior portions of the frontal sinus. However, in the last 10 years, transorbital neuroendoscopic surgery (TONES) has been used for complex frontal sinus cases (i.e., lateral lesions, unfavorable frontal sinus anatomy). The objective of this study is to describe our experience using the TONES in the frontal sinus and the step-by-step technique used. Materials and Methods: We reviewed the charts from the patients who underwent management of frontal sinus lesion with a TONES, being performed from 2017 to 2022. Demographic data was obtained, as well as information on surgical approach, intraoperative findings, ability to access the target, complications and recurrence. Results: Six patients and seven surgeries for frontal sinus lesions were operated through the TONES. All patients had TONES combined with transnasal endoscopic approach, the majority on the left side (5/7, 71%). The average age was 43.7; there were five male and one female patient. The most common symptoms leading to indication for surgery were headaches or a sensation of pressure in the forehead (5/7; 71%). In this case series, the most common etiology was mucocele (5/7, 71%). The length of stay in all the cases was one day. The most common complication was mild and transitory V1 numbness, which fully recovered after six months (5/7, 71%). mucocele was presented after transorbital surgery in 2 patients. In 6 of all, gross total resection was achieved. Our average follow-up was 32 months (range: 6–48 months). Conclusion: TONES are valuable complement endoscopic transnasal approaches for treating frontal sinus lesions, mainly when located in anatomically or technically challenging regions. Our initial series shows that transorbital approaches are feasible and can be safely performed, presented with reversible V1 numbness without orbital damage, visual change, ptosis, or permanent diplopia. Also, in most cases, this surgery provides a successful and definite treatment. Publication History Article published online: 01 February 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".