The Supraorbital Approach for Recurrent or Residual Suprasellar Tumors
Bibliographic record
Abstract
BACKGROUND: Suprasellar tumors can be removed through a variety of approaches including conventional frontotemporal craniotomies, the transsphenoidal route, or the supraorbital (SO) eyebrow craniotomy. Herein we assess the utility of the SO route for recurrent or residual suprasellar tumors previously treated by an alternative route. MATERIAL AND METHODS: A retrospective analysis of all consecutive patients who underwent an SO approach for removal of a recurrent/residual tumor was undertaken. RESULTS: Between December 2007 and February 2010, 11 patients underwent an SO craniotomy for a recurrent or growing residual tuberculum sellae meningioma (n=7) or craniopharyngioma (n=4). All 11 patients had prior craniotomies, 5 had transsphenoidal surgery, 6 had radiation treatment, and 1 had chemotherapy. In the last 5 cases, the endoscope was used in addition to the microscope for intraoperative visualization. 3 patients underwent decompression of multicystic craniopharyngiomas and the remaining 8 patients had tumor debulking, all achieving 70% or more tumor removal. Of 9 patients with preoperative visual deterioration, 6 (67%) had improvement and no patient had visual worsening. No new adenohypophysis or neurohypophysis dysfunction was noted. One patient had a postoperative CSF leak requiring reoperation. CONCLUSION: The SO approach should be considered as a safe and effective alternative route for recurrent or residual suprasellar tumors previously treated by conventional craniotomy or TS surgery. It typically offers a simplified trajectory that minimizes scar tissue from prior approaches and provides excellent access for optic apparatus decompression. Endoscopy is helpful to visualize hidden tumor remnants and maximize safe tumor removal.
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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 | 0.000 | 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) | 0.003 | 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".