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Record W2020190200 · doi:10.17712/nsj.2015.2.20140621

Focal brainstem gliomas

2015· review· en· W2020190200 on OpenAlexaff
Abdulrahman J. Sabbagh, Ahmed Alaqeel

Bibliographic record

VenueNeurosciences · 2015
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of Calgary
FundersDeanship of Scientific Research, King Saud UniversityKing Fahad Medical CityKing Saud University
KeywordsNeuronavigationBrainstemMedicineGliomaRadiologyIntraoperative neurophysiological monitoringMagnetic resonance imagingSurgery

Abstract

fetched live from OpenAlex

Improved neuronavigation guidance as well as intraoperative imaging and neurophysiologic monitoring technologies have enhanced the ability of neurosurgeons to resect focal brainstem gliomas. In contrast, diffuse brainstem gliomas are considered to be inoperable lesions. This article is a continuation of an article that discussed brainstem glioma diagnostics, imaging, and classification. Here, we address open surgical treatment of and approaches to focal, dorsally exophytic, and cervicomedullary brainstem gliomas. Intraoperative neuronavigation, intraoperative neurophysiologic monitoring, as well as intraoperative imaging are discussed as adjunctive measures to help render these procedures safer, more acute, and closer to achieving surgical goals.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.173
GPT teacher head0.415
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2015
Admission routes1
Has abstractyes

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