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Record W3092955570 · doi:10.3390/cancers12102999

Management of Cancer-Associated Thrombosis: An Evolving Area

2020· editorial· en· W3092955570 on OpenAlexaff
Corinne Frère, Jean M. Connors, Dominique Farge

Bibliographic record

VenueCancers · 2020
Typeeditorial
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineIntensive care medicineMultidisciplinary approachThrombosisCancerVenous thromboembolismLow molecular weight heparinVenous thrombosisDrugBioinformaticsSurgeryInternal medicinePharmacology

Abstract

fetched live from OpenAlex

The management of cancer-associated thrombosis (CAT) is an evolving area. With the use of direct oral anticoagulants as a new option in the management of CAT, clinicians now face several choices for the individual cancer patient with venous thromboembolism. A personalized approach, matching the right drug to the right patient, based on drug properties, efficacy and safety, side effect profile of each drug, and patient values and preference, will probably supplant the one size fits all approach of use of only low-molecular-weight heparin in the near future. We herein present eight translational, clinical research, and review articles on recent advances in the management of CAT published in the Special Issue “Treatment for Cancer-Associated Thrombosis” of Cancers. For now, a multidisciplinary patient-centered approach involving a close cooperation between oncologists and other specialists is warranted to guide clinical decision making and optimize the treatment of VTE in cancer patient.

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)0.004
metaresearch head score (Gemma)0.014
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0050.003

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 head0.317
Teacher spread0.293 · 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
GenreEditorial

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

Citations3
Published2020
Admission routes1
Has abstractyes

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