Transection of Inferior Orbital Fissure Contents for Improved Access and Visibility in Orbital Surgery
Bibliographic record
Abstract
BACKGROUND: Selective inferior orbital fissure (IOF) content transection for the purpose of surgical access to the posterior orbital floor is a technique that facilitates visualization of the posterior bony ledges of traumatic orbital floor defects. It also has potential advantages in achieving stable placement of reconstructive materials. Although not new, the surgical technique has not yet been described, and the morbidity of the technique has not been quantified. This article describes the procedure and assesses the morbidity specific to the division of related neural structures. METHODS: The technique and surgical anatomy are described and illustrated with intraoperative photographs. Postoperative assessment of neural structures relevant to the division of IOF contents is performed. These values are compared with the nonoperated side to evaluate the morbidity of the technique. RESULTS: The technique, which is consistently used by the senior author in the repair of orbital floor defects with very small posterior ledges or which extend to and involve the IOF, facilitates better visualization of the posterior ledge and posterolateral ledge in such cases. Surgical outcomes including facial sensation and lacrimal function on the operated side remain within the reference range and are not significantly different when compared with the contralateral nonoperated side. CONCLUSIONS: Selective IOF transection aids in the direct visualization of the posterior bony ledges in the repair of posterior orbital floor defects. It therefore may facilitate the placement of reconstructive materials on bony ledges circumferentially, providing stable reconstruction, potentially reducing implant-related complications without causing increased morbidity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".