Multimodal multidisciplinary surgical approach for the treatment of pituitary tumours.
Bibliographic record
Abstract
OBJECTIVES: The surgical management of pituitary tumours is being impacted by the development of two key technologies: image guidance and endoscopy. This study sought to assess their impact. METHODS: Retrospective review of all patients referred to the Skull Base Clinic of the McGill University Health Centre since 2000. Patients were operated on in a multidisciplinary context using a multimodal approach combining endoscopy and microscopy. Imaging during the surgery was initially supported by both three-dimensional neuronavigation and traditional C-arm fluoroscopy. RESULTS: Seventy-five patients were referred to the multidisciplinary clinic, for a total of 41 surgeries. Neuronavigation was used in all cases. C-arm fluoroscopy was not found to improve our surgeries and was removed from our protocol. Endoscopy was found to be advantageous as it allowed improved visualization. It also permitted identifying surrounding structures in the lateral wall of the sphenoid sinus, next to the tumour, and "around corners." Moreover, it encouraged multidisciplinary co-operation as it allowed neurosurgeons and otolaryngologists to follow progress during the case. Nevertheless, the microscope continued to play a role as it facilitated a bimanual technique, stable magnification, and a three-dimensional view. Morbidities in our case series appeared to be minimal. CONCLUSION: Both endoscopy and the microscope were found to have a role in our surgeries. We consider these technologies to be complementary. C-arm fluoroscopy was rendered obsolete by the neuronavigation unit. A multidisciplinary, multimodal approach maximizes the benefits of these new technologies and permits the best surgical result.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".