MétaCan
Menu
Back to cohort
Record W3106792472 · doi:10.1136/esmoopen-2020-000948

Prevention of venous thromboembolism in ambulatory patients with cancer

2020· review· en· W3106792472 on OpenAlexaff
Alok A. Khorana, Alexander T. Cohen, Marc Carrier, Guy Meyer, Ingrid Pabinger, Petr Kavan, PhilipS Wells

Bibliographic record

VenueESMO Open · 2020
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsJewish General HospitalMcGill UniversityOttawa HospitalUniversity of Ottawa
FundersBayer
KeywordsMedicineApixabanAmbulatoryRivaroxabanCancerLow molecular weight heparinWarfarinAnticoagulantRegimenIntensive care medicineThrombosisComplicationVenous thromboembolismClinical trialVenous thrombosisInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

Patients with cancer are at high risk of venous thromboembolic events, and this risk can be further increased in patients with certain cancer types and by cancer treatments. Guidelines on the prevention of cancer-associated thrombosis (CAT) recommend thromboprophylaxis for hospitalised patients; however, this is not routinely recommended for ambulatory patients receiving chemotherapy and is limited to specified high-risk patients. Identification of the ambulatory patients at risk of CAT who would most benefit from anticoagulant therapy is therefore critical to reduce the incidence of this complication. For patients receiving thromboprophylaxis for CAT, treatment options include low molecular weight heparin, acetylsalicylic acid, warfarin or direct oral anticoagulants (apixaban or rivaroxaban), dependent on the cancer type and cancer treatment regimen. This review discusses emerging clinical trial data and their potential clinical impact.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0050.001

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 head0.052
GPT teacher head0.376
Teacher spread0.324 · 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 designSystematic review
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

Citations26
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueESMO OpenSame topicVenous Thromboembolism Diagnosis and ManagementFrench-language works237,207