Endoscopic‐assisted transorbital extended orbital exenteration: A multi‐institutional preclinical study
Bibliographic record
Abstract
BACKGROUND: Sinonasal malignancies with orbital invasion have dismal prognosis even when treated with orbital exenteration (OE). Sugawara et al. developed a surgical strategy called "extended-OE (EOE)," showing encouraging outcomes. We hypothesized that a similar resection is achievable under endoscopic guidance through the exenterated orbit (endoscopic-EOE). METHODS: The study was conducted in three institutions: University of Vienna; Mayo Clinic; University of Insubria; 48 orbital dissections were performed. A questionnaire was developed to evaluate feasibility and safety of each step, scoring from 1 to 10, ("impossible" to "easy," and "high risk" to "low risk," respectively), most likely complication(s) were hypothesized. RESULTS: The step-by-step technique is thoroughly described. The questionnaire was answered by 25 anterior skull base surgeons from six countries. Mean, median, range, and interquartile range of both feasibility and safety scores are reported. CONCLUSIONS: Endoscopic-EOE is a challenging but feasible procedure. Clinical validation is required to assess real-life outcomes.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".