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What is the role of hydroxychloroquine in reducing thrombotic risk in patients with antiphospholipid antibodies?

2016· review· en· W2558859036 on OpenAlexaff
Tzu‐Fei Wang, Wendy Lim

Bibliographic record

VenueHematology · 2016
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHydroxychloroquineMedicineAntibodyAntiphospholipid syndromeThrombosisImmunologyInternal medicineDiseaseCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Abstract A 35-year-old man presents with an acute unprovoked deep vein thrombosis of the left lower extremity. He is treated with anticoagulation and elects to discontinue treatment after 6 months. He subsequently develops polyarthralgias, fatigue, and a malar rash, and a diagnosis of systemic lupus erythematosus is made based on laboratory and clinical findings. Additional laboratory testing reveals persistent triple positive antiphospholipid antibodies, including lupus anticoagulant, high titer anticardiolipin antibodies, and anti–β2-glycoprotein I antibodies. The patient is reinitiated on anticoagulation, and the patient’s rheumatologist inquires if the addition of hydroxychloroquine could help to prevent recurrent thrombosis.

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)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.319
Teacher spread0.301 · 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

Citations30
Published2016
Admission routes1
Has abstractyes

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