The Benefit of Intraoperative Magnetic Resonance Imaging in Endoscopic and Microscopic Transsphenoidal Resection of Recurrent Pituitary Adenomas
Bibliographic record
Abstract
The surgical treatment of recurrent adenomas can be challenging. Intraoperative magnetic resonance imaging (iMRI) can improve the orientation and increase the safe extent of resection. We conducted a quantitative and qualitative retrospective analysis of recurrent adenomas treated by endoscopic or microscopic iMRI-assisted transsphenoidal surgery. A total number of 59 resections were selected. Detailed volumetric measurements, tumor characteristics, and MRI features of intraoperative remnants were evaluated. Intraoperative MRI increased the gross total resection (GTR) rate from 33.9% to 49.2%. Common locations of tumor remnants after iMRI were the clivus, the wall of the cavernous sinus or the perforation of the diaphragm. Increasing tumor volume and the microscopic technique were significantly associated with further resection after iMRI in the univariate analysis (p = 0.004, OR 1.6; p = 0.009, OR 4.4). Only the increasing tumor volume was an independent predictor for further resection (p = 0.007, OR 1.5). A significantly higher proportion of GTRs was achieved with the endoscopic technique (p = 0.001). Patients with a large recurrent pituitary adenoma who underwent microscopic transsphenoidal resection were the most likely to benefit from iMRI regarding the extent of resection. Occult invasions of the cavernous sinus and/or the clivus were the most common findings leading to further resection of tumor remnants after iMRI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".