Development of an Orbital Endoscope for Use with the Free Electron Laser
Bibliographic record
Abstract
PURPOSE: To explore the feasibility of designing, constructing, and testing an orbital endoscope for use with the free electron laser (FEL). METHODS: An experimental study with an author-designed laser delivery system through an endoscope was conducted. Two adult pig cadavers and 6 fresh human cadavers had orbital endoscopy performed to develop a method of optic nerve sheath fenestration (ONSF) with the FEL. Twelve orbits were used to develop the surgical procedure by comparing visualization media and surgical technique. In the first 7 trials, a different variable was changed; the procedure was then refined in the human cadaver experiments. An ONSF was performed with the FEL (6.45 microm, 30 Hz, 2 to 3 mJ, 250-microm spot size) through a glass hollow wave guide introduced through an endoscope in 4 human cadaver orbits. RESULTS: Visualization of the orbital structures was clearest with carbon dioxide; sodium hyaluronate did not displace the fat, and saline hydrated the orbital fat. Biopsy forceps alone did not produce a dural window in the four trials that used the forceps. A dural window was made by using the FEL through a glass hollow wave guide adapted to the Olympus HYF-XP endoscope in the second, third, fifth, and sixth human trials with the FEL. Histologic evidence of the ONSF was produced. CONCLUSIONS: A hollow wave guide capable of transmitting the FEL through an endoscope was successfully constructed. ONSF with the FEL applied through an endoscope is technically feasible. Additional studies are currently examining techniques to improve intraorbital endoscopy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".