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Record W3188668922 · doi:10.1111/jth.15491

Long‐term risk of recurrent venous thromboembolism among patients receiving extended oral anticoagulant therapy for first unprovoked venous thromboembolism: A systematic review and meta‐analysis

2021· review· en· W3188668922 on OpenAlexafffund
Faizan Khan, Tobias Tritschler, Miriam Kimpton, Philip S. Wells, Clive Kearon, Jeffrey I. Weitz, Harry R. Büller, Gary E. Raskob, Walter Ageno, Françis Couturaud, Paolo Prandoni, Gualtiero Palareti, Cristina Legnani, Paul A. Kyrle, Sabine Eichinger, Lisbeth Eischer, Cecilia Becattini, Giancarlo Agnelli, Maria Cristina Vedovati, Geert‐Jan Geersing, Toshihiko Takada, Benilde Cosmi, Drahomir Aujesky, Letizia Marconi, Antonio Palla, Sergio Siragusa, Charlotte Bradbury, Sameer Parpia, Ranjeeta Mallick, Anthonie W.A. Lensing, Martin Gebel, Michael Grosso, Minggao Shi, Kednapa Thavorn, Brian Hutton, Grégoire Le Gal, Marc Rodger, Dean Fergusson

Bibliographic record

VenueJournal of Thrombosis and Haemostasis · 2021
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversityMcGill UniversityImpactUniversity of OttawaThrombosis and Atherosclerosis Research InstituteOttawa Hospital
FundersCanadian Institutes of Health ResearchUniversity of OttawaMcGill UniversityHeart and Stroke Foundation of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMedicinePulmonary embolismIncidence (geometry)Meta-analysisVenous thromboembolismCumulative incidenceInternal medicineCohort studyCase fatality ratePediatricsProspective cohort studyCohortSurgeryThrombosisEpidemiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.026
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.094
GPT teacher head0.373
Teacher spread0.279 · 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 designMeta-analysis
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

Citations50
Published2021
Admission routes2
Has abstractno

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