Clinical Landscape of Pituitary Adenoma
Bibliographic record
Abstract
Background: Pituitary adenomas are common neoplasms of the sellar region, occurring in up to 25% of the population. While most are asymptomatic, they can lead to visual decline and endocrinopathies which often necessitate surgical resection. Importantly, there is lack of established clinical predictors of relevant postoperative outcomes for these patients and there is a need for a robust, consecutively treated cohort with detailed clinical annotation for further exploration. Methods: We retrospectively reviewed pituitary adenomas that underwent surgical resection at our institution between 2000 and 2015, inclusive. Covariates of postoperative progression, endocrine cure, CSF leak, and visual improvement were identified using logistic regression modeling. Survival analyses were performed using Kaplan–Meier and Cox proportional hazards survival modeling. Results: Overall, 431 patients were included in our cohort. Subtotal resection (OR: 4.50, p < 0.001) and higher MIB-1 proliferative index (OR: 2.36, p = 0.001) were independently associated with postoperative progression in a multivariate logistic regression model. The same variables were significantly associated with progression-free survival in a multivariate Cox regression model. Smaller preoperative size (OR: 0.37, p = 0.031) and gross total resection (OR: 0.20, p = 0.005) were independently predictive of postoperative endocrine cure. Intraoperative CSF leak was the only predictor of postoperative CSF leak (OR: 7.09, p < 0.001), and no individual variables were predictive of postoperative visual improvement. Conclusions: We examine the clinical landscape of surgically resected pituitary adenoma to better inform surgical decision making, prognostication, and patient counseling. Publication History Article published online: 05 February 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".