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Record W2549399469 · doi:10.1080/17474086.2017.1257935

Controversies in the management of cancer-associated thrombosis

2016· review· en· W2549399469 on OpenAlexaff
Marc Carrier, Paolo Prandoni

Bibliographic record

VenueExpert Review of Hematology · 2016
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineCancerIntensive care medicineThrombosisPulmonary embolismComplicationLow molecular weight heparinChemotherapyHeparinSurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Cancer associated thrombosis (CAT) is a frequent complication among cancer patients. It is associated with increased morbidity, mortality, and psychological burden. Areas covered: Low-molecular-weight heparin monotherapy for the initial 6 months is considered the standard of care for the acute and long-term management of CAT. For patients at high risk of recurrent CAT (e.g. active cancer or still undergoing anticancer therapy) beyond the initial 6 months of treatment, continuation of anticoagulation therapy for secondary prevention is usually recommended. The management of anticoagulation therapy is more challenging in patients with cancer. Cancer patients are more likely to have recurrent events despite anticoagulation, thrombocytopenia due to their chemotherapy regimens or have incidental pulmonary embolism diagnosed on their staging imaging. Expert commentary: We will review expert consensuses and opinions in order to guide clinicians on how to tailor the management of CAT in these special circumstances.

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)0.003
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.042
GPT teacher head0.411
Teacher spread0.369 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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