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Record W1965653930 · doi:10.1227/neu.0000000000000633

Stereotactic Radiosurgery for Intracranial Meningiomas

2015· article· en· W1965653930 on OpenAlexaff
Alireza Mansouri, Daipayan Guha, Soroush Larjani, Gelareh Zadeh, Douglas Kondziolka

Bibliographic record

VenueNeurosurgery · 2015
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsRadiosurgeryMedicineModalitiesLesionMeningiomaRadiologySurgeryRadiation therapy

Abstract

fetched live from OpenAlex

Meningiomas are among the most common adult brain tumors. Although the optimal management of meningiomas would provide complete elimination of the lesion, this cannot always be accomplished safely through resection. Therefore, other therapeutic modalities, such as stereotactic radiosurgery (as primary or adjunctive therapy), have emerged. In the current review, we have provided an overview of the historical outcomes of various radiosurgical modalities applied in the management of meningiomas. Furthermore, we provide a discussion on key factors (eg World Health Organization grade, lesion size, and lesion location) that affect tumor control and adverse event rates. We discuss recent changes in our understanding of meningiomas, based on molecular and genetic markers, and how these will change our perspective on the management of meningiomas. We conclude by outlining the areas in which knowledge gaps persist and provide suggestions as to how these can be addressed. ABBREVIATIONS: ARE, adverse radiation event CI, conformity index FSRT, fractionated radiotherapy GK, Gamma Knife GTR, gross total resection HGM, high-grade meningioma LINAC, linear accelerator NF2, neurofibromatosis type 2 OA, optic apparatus OS, overall survival PFS, progression-free survival SRS, stereotactic radiosurgery STR, subtotal resection TC, tumor control VS, vestibular schwannoma WHO, World Health Organization

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

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

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 head0.079
GPT teacher head0.298
Teacher spread0.220 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

Quick stats

Citations28
Published2015
Admission routes1
Has abstractyes

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