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Record W4367060277 · doi:10.1093/neuonc/noad059

Epidemiology, biology, and management of venous thromboembolism in gliomas: An interdisciplinary review

2023· article· en· W4367060277 on OpenAlexaff
Jasmin Jo, María Díaz, Craig Horbinski, Nigel Mackman, Stephen Bagley, Marika Broekman, Janusz Rak, James Perry, Ingrid Pabinger, Nigel S. Key, David Schiff

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsSunnybrook Health Science CentreMcGill University
FundersNational Institute of Neurological Disorders and StrokeNational Heart, Lung, and Blood InstituteNorthwestern University
KeywordsMedicineGliomaInternal medicineLow molecular weight heparinOncologyRisk factorIntensive care medicineThrombosisCancer research

Abstract

fetched live from OpenAlex

Patients with diffuse glioma are at high risk of developing venous thromboembolism (VTE) over the course of the disease, with up to 30% incidence in patients with glioblastoma (GBM) and a lower but nonnegligible risk in lower-grade gliomas. Recent and ongoing efforts to identify clinical and laboratory biomarkers of patients at increased risk offer promise, but to date, there is no proven role for prophylaxis outside of the perioperative period. Emerging data suggest a higher risk of VTE in patients with isocitrate dehydrogenase (IDH) wild-type glioma and the potential mechanistic role of IDH mutation in the suppression of production of the procoagulants tissue factor and podoplanin. According to published guidelines, therapeutic anticoagulation with low molecular weight heparin (LMWH) or alternatively, direct oral anticoagulants (DOACs) in patients without increased risk of gastrointestinal or genitourinary bleeding is recommended for VTE treatment. Due to the elevated risk of intracranial hemorrhage (ICH) in GBM, anticoagulation treatment remains challenging and at times fraught. There are conflicting data on the risk of ICH with LMWH in patients with glioma; small retrospective studies suggest DOACs may convey lower ICH risk than LMWH. Investigational anticoagulants that prevent thrombosis without impairing hemostasis, such as factor XI inhibitors, may carry a better therapeutic index and are expected to enter clinical trials for cancer-associated thrombosis.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.063
GPT teacher head0.429
Teacher spread0.366 · 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 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".

Quick stats

Citations30
Published2023
Admission routes1
Has abstractyes

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