Stereotactic radiosurgery for secretory pituitary adenomas: systematic review and International Stereotactic Radiosurgery Society practice recommendations
Bibliographic record
Abstract
OBJECTIVE: A systematic review was performed to provide objective evidence on the use of stereotactic radiosurgery (SRS) in the management of secretory pituitary adenomas and develop consensus recommendations. METHODS: The authors performed a systematic review of the English-language literature up until June 2018 using the PRISMA guidelines. The PubMed (Medline), Embase, and Cochrane databases were searched. A total of 45 articles reporting single-institution outcomes of SRS for acromegaly, Cushing's disease, and prolactinomas were selected and included in the analysis. RESULTS: For acromegaly, random effects meta-analysis estimates for crude tumor control rate, crude endocrine remission rate, and any new hypopituitarism rates were 97.0% (95% CI 96.0%-98.0%), 44.0% (95% CI 35.0%-53.0%), and 17.0% (95% CI 13.0%-23.0%), respectively. For Cushing's disease, random effects estimates for crude tumor control rate, crude endocrine remission rate, and any new hypopituitarism rate were 92.0% (95% CI 87.0%-95.0%), 48.0% (95% CI 35.0%-61.0%), and 21.0% (95% CI 13.0%-31.0%), respectively. For prolactinomas, random effects estimates for crude tumor control rate, crude endocrine remission rate, and any new hypopituitarism rate were 93.0% (95% CI 90.0%-95.0%), 28.0% (95% CI 19.0%-39.0%), and 12.0% (95% CI 6.0%-24.0%), respectively. Meta-regression analysis did not show a statistically significant association between mean margin dose with crude endocrine remission rate or mean margin dose with development of any new hypopituitarism rate for any of the secretory subtypes. CONCLUSIONS: SRS offers effective tumor control of hormone-producing pituitary adenomas in the majority of patients but a lower rate of endocrine improvement or remission.
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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 | 0.032 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".