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Record W1660836938 · doi:10.3171/2014.2.jns13139

Gamma Knife radiosurgery for sellar and parasellar meningiomas: a multicenter study

2014· article· en· W1660836938 on OpenAlexaff
Jason P. Sheehan, Robert M. Starke, Hideyuki Kano, Anthony M. Kaufmann, David Mathieu, Frederick A. Zeiler, Michael A. West, Samuel T. Chao, Gandhi Varma, Veronica Chiang, James B. Yu, Heyoung L. McBride, Peter Nakaji, Emad Youssef, Norissa Honea, Stephen Rush, Douglas Kondziolka, John Y. K. Lee, Robert L. Bailey, Sandeep Kunwar, Paula L. Petti, L. Dade Lunsford

Bibliographic record

VenueJournal of neurosurgery · 2014
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsUniversité de SherbrookeUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsMedicineRadiosurgeryNeurovascular bundleMeningiomaSurgerySella turcicaRadiologyUnivariate analysisMultivariate analysisRadiation therapyInternal medicine

Abstract

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OBJECT: Parasellar and sellar meningiomas are challenging tumors owing in part to their proximity to important neurovascular and endocrine structures. Complete resection can be associated with significant morbidity, and incomplete resections are common. In this study, the authors evaluated the outcomes of parasellar and sellar meningiomas managed with Gamma Knife radiosurgery (GKRS) both as an adjunct to microsurgical removal or conventional radiation therapy and as a primary treatment modality. METHODS: A multicenter study of patients with benign sellar and parasellar meningiomas was conducted through the North American Gamma Knife Consortium. For the period spanning 1988 to 2011 at 10 centers, the authors identified all patients with sellar and/or parasellar meningiomas treated with GKRS. Patients were also required to have a minimum of 6 months of imaging and clinical follow-up after GKRS. Factors predictive of new neurological deficits following GKRS were assessed via univariate and multivariate analyses. Kaplan-Meier analysis and Cox multivariate regression analysis were used to assess factors predictive of tumor progression. RESULTS: The authors identified 763 patients with sellar and/or parasellar meningiomas treated with GKRS. Patients were assessed clinically and with neuroimaging at routine intervals following GKRS. There were 567 females (74.3%) and 196 males (25.7%) with a median age of 56 years (range 8-90 years). Three hundred fifty-five patients (50.7%) had undergone at least one resection before GKRS, and 3.8% had undergone prior radiation therapy. The median follow-up after GKRS was 66.7 months (range 6-216 months). At the last follow-up, tumor volumes remained stable or decreased in 90.2% of patients. Actuarial progression-free survival rates at 3, 5, 8, and 10 years were 98%, 95%, 88%, and 82%, respectively. More than one prior surgery, prior radiation therapy, or a tumor margin dose < 13 Gy significantly increased the likelihood of tumor progression after GKRS. At the last clinical follow-up, 86.2% of patients demonstrated no change or improvement in their neurological condition, whereas 13.8% of patients experienced symptom progression. New or worsening cranial nerve deficits were seen in 9.6% of patients, with cranial nerve (CN) V being the most adversely affected nerve. Functional improvements in CNs, especially in CNs V and VI, were observed in 34% of patients with preexisting deficits. New or worsened endocrinopathies were demonstrated in 1.6% of patients; hypothyroidism was the most frequent deficiency. Unfavorable outcome with tumor growth and accompanying neurological decline was statistically more likely in patients with larger tumor volumes (p = 0.022) and more than 1 prior surgery (p = 0.021). CONCLUSIONS: Gamma Knife radiosurgery provides a high rate of tumor control for patients with parasellar or sellar meningiomas, and tumor control is accompanied by neurological preservation or improvement in most patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.275
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations102
Published2014
Admission routes1
Has abstractyes

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