Combined Exoscopic and Endoscopic Technique for Craniofacial Resection
Bibliographic record
Abstract
We determined the feasibility of the combined exoscopic-endoscopic technique (CEE) as an alternative to the microscope in craniofacial resection (CFR). This retrospective study was conducted at a single institution and included eight consecutive patients with head and neck tumors who underwent CFR between September 2019 and July 2021. During the transcranial approach, microsurgery was performed using an exoscope in the same manner as in traditional microscopic surgery, and an endoscope was used at the blind spot of the exoscope. The exoscope provided images of sufficient quality to perform microsurgery, while the sphenoid sinus lumen was the blind spot of the exoscope during anterior (n = 3) and anterolateral CFR (n = 2), and the medial aspect of the temporal bone was the blind spot of the exoscope during temporal bone resection (n = 2). These blind spots were visualized by the endoscope to facilitate accurate transection of the skull base. The advantages of the exoscope and endoscope include compact size, ergonomics, surgical field accessibility, and equal visual experience for neurosurgeons and head and neck surgeons, which enabled simultaneous transcranial and transfacial surgical procedures. All the surgeries were successful without any relevant complications. CEE is effective in transcranial skull base surgery, especially CFR involving simultaneous surgical procedures.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".