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Record W2793259357 · doi:10.1182/blood-2017-09-805689

Antiphospholipid antibodies and recurrent thrombosis after a first unprovoked venous thromboembolism

2018· article· en· W2793259357 on OpenAlexafffund
Clive Kearon, Sameer Parpia, Frederick A. Spencer, Trevor Baglin, Scott M. Stevens, Kenneth A. Bauer, Steven R. Lentz, Craig M. Kessler, James D. Douketis, Stephan Moll, Scott Kaatz, Sam Schulman, Jean M. Connors, Jeffrey S. Ginsberg, Luciana Spadafora, Vinai Bhagirath, Patricia C. Liaw, Jeffrey I. Weitz, Jim A. Julian

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineVenous thromboembolismThrombosisAntiphospholipid syndromeVenous thrombosisD-dimerAntibodyThrombophiliaInternal medicineImmunology

Abstract

fetched live from OpenAlex

= .006) in the 3.8% of patients with 2 or 3 different APA types on either the same or different occasions. There was no association between having an APA and D-dimer levels. We conclude that having the same type of APA on 2 occasions or having >1 type of APA on the same or different occasions is associated with recurrent thrombosis in patients with a first unprovoked VTE who stop anticoagulant therapy in response to negative D-dimer tests. APA and D-dimer levels seem to be independent predictors of recurrence in patients with an unprovoked VTE. This trial was registered at www.clinicaltrials.gov as #NCT00720915.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.014
GPT teacher head0.261
Teacher spread0.248 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations80
Published2018
Admission routes2
Has abstractyes

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