Hemi-transseptal Approach for Pituitary Surgery: A Follow-Up Study
Bibliographic record
Abstract
<b>Objectives</b> 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. <b>Design</b> A retrospective case-control study. <b>Setting</b> Montreal Neurological Institute and Jewish General Hospital, Montreal, Canada. <b>Participants</b> Patients who underwent endoscopic endonasal transsphenoidal approach to skull base pathology. <b>Main Outcome Measures</b> Operative time, CSF rhinorrhea, and postoperative nasal morbidity. <b>Results</b> 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; <i>p</i> = 0.048). The rates of nasal morbidity (septal perforation [5/102 vs. 6/37; <i>p</i> = 0.029] and mucosal adhesion [11/102 vs. 10/39 <i>p</i> = 0.027]), fascia lata harvest (21/100 vs. 18/39; <i>p</i> = 0.0028), and postoperative CSF leak rates (7/100 vs. 9/38; <i>p</i> = 0.006) were lower in the Hemi-T group. <b>Conclusion</b> 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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".