Hemi-transseptal Approach for Pituitary Surgery: A Follow-Up Study
Bibliographic record
Abstract
Objectives The hemi-transseptal (Hemi-T) approach was developed to overcome the potential drawbacks of the nasoseptal flap (NSF) in endoscopic endonasal transsphenoidal skull base surgery. In this study, we describe further refinements on the Hemi-T approach, and report long-term outcomes as compared with traditional methods of skull base reconstruction. Design A retrospective case-control study. Setting Montreal Neurological Institute and Jewish General Hospital, Montreal, Canada. Participants Patients who underwent endoscopic endonasal transsphenoidal approach to skull base pathology. Main Outcome Measures Operative time, CSF rhinorrhea, and postoperative nasal morbidity. Results A total of 105 patients underwent the Hemi-T approach versus 40 controls. Operative time was shorter using the Hemi-T technique (180.51 ± 56.9 vs. 202.9 ± 62 minutes; p = 0.048). The rates of nasal morbidity (septal perforation [5/102 vs. 6/37; p = 0.029] and mucosal adhesion [11/102 vs. 10/39 p = 0.027]), fascia lata harvest (21/100 vs. 18/39; p = 0.0028), and postoperative CSF leak rates (7/100 vs. 9/38; p = 0.006) were lower in the Hemi-T group. Conclusion Advantages of the Hemi-T approach over traditional exposure techniques include preservation of the nasal vascular pedicle, shorter operative time, reduced fascia lata harvest rates, and decreased nasal 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".