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Record W2612885052 · doi:10.1182/blood-2017-03-742304

How I treat recurrent venous thromboembolism in patients receiving anticoagulant therapy

2017· review· en· W2612885052 on OpenAlexaff
Sam Schulman

Bibliographic record

VenueBlood · 2017
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversityThrombosis and Atherosclerosis Research Institute
Fundersnot available
KeywordsMedicineIntensive care medicineVenous thromboembolismRegimenDiseaseDosingClinical trialAnticoagulantDrugAnticoagulant therapySurgeryInternal medicineThrombosisPharmacology

Abstract

fetched live from OpenAlex

Oral anticoagulant therapy for venous thromboembolism is very effective. When oral anticoagulants are managed well, the risk of recurrence is approximately 2 per 100 patient-years. The main reasons for a breakthrough event are underlying disease and subtherapeutic drug levels. The most common underlying disease that results in recurrence on treatment is cancer. Subtherapeutic drug levels can be caused by poor adherence to the drug regimen, interactions with other drugs or food, or inappropriate dosing. It is important to investigate and understand the cause whenever such an event occurs and to improve management of anticoagulants thereby avoiding further recurrences. Here we present 4 illustrative cases together with a discussion of the underlying pathology. Whereas the mechanisms are usually quite well understood, the management of further anticoagulation after a breakthrough event is based on minimal or no clinical trial evidence.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.099
GPT teacher head0.351
Teacher spread0.252 · 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

Citations60
Published2017
Admission routes1
Has abstractyes

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