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Record W1864637727 · doi:10.3171/2015.5.focus1514

Imaging diagnosis and the role of endovascular embolization treatment for vascular intraspinal tumors

2015· review· en· W1864637727 on OpenAlexafffund
Marlise P. dos Santos, Jingwen Zhang, Diana Ghinda, Rafael Glikstein, Ronit Agid, Georges Rodesch, Donatella Tampieri, Karel G. terBrugge

Bibliographic record

VenueNeurosurgical FOCUS · 2015
Typereview
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversity of TorontoUniversity Health NetworkOttawa HospitalUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsMedicineVascularityEmbolizationRadiologyAngiographyEndovascular treatmentBlood flowMagnetic resonance imagingAneurysm

Abstract

fetched live from OpenAlex

Intraspinal tumors comprise a large spectrum of neoplasms, including hemangioblastomas, paragangliomas, and meningiomas. These tumors have several common characteristic imaging features, such as highly vascular mass appearance in angiography, hypointense rim and serpentine flow voids in MRI, and intense enhancement after intravenous contrast administration. Due to their rich vascularity, these tumors represent a special challenge for surgical treatment. More recently, the surgical treatment of intraspinal vascular tumors has benefited from the combination of endovascular techniques used to better delineate these lesions and to promote preoperative reduction of volume and tissue blood flow. Endovascular embolization has been proven to be a safe procedure that facilitates the resection of these tumors; hence, it has been proposed as part of the standard of care in their management.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.041
GPT teacher head0.315
Teacher spread0.274 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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