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Record W2133473048 · doi:10.1097/moh.0b013e3282a4a65f

Antiphospholipid antibiodies: a critical review of the literature

2007· review· en· W2133473048 on OpenAlexafffund
Wendy Lim, Mark Crowther

Bibliographic record

VenueCurrent Opinion in Hematology · 2007
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsSt. Joseph’s Healthcare HamiltonSt. Joseph's Hospital
FundersHeart and Stroke Foundation of Canada
KeywordsMedicineIntensive care medicineMEDLINEInternal medicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The antiphospholipid antibody syndrome is defined by the presence of antiphospholipid antibodies in patients with recurrent venous or arterial thromboembolism or pregnancy morbidity. Antithrombotic therapies are the mainstay of treatment to reduce the risk of recurrent thromboembolism that characterizes this condition. RECENT FINDINGS: Limitations in the laboratory testing for antiphospholipid antibodies and changes in the diagnostic criteria for antiphospholipid antibody syndrome are important to recognize as they will affect the type of patients enrolled in the clinical trials evaluating antiphospholipid antibody syndrome therapies. In this review, we discuss the laboratory testing and diagnostic criteria for antiphospholipid antibody syndrome, and examine the evidence supporting the optimal antithrombotic management of patients with antiphospholipid antibodies. Studies addressing the management of patients with antiphospholipid antibodies and venous thromboembolism, and antiphospholipid antibodies and ischemic stroke, will be critically reviewed. SUMMARY: Clinical trials and observational studies have evaluated the optimal type, intensity and duration of anticoagulant therapy in patients with antiphospholipid antibodies. Critical evaluation of these studies is required to assess the generalizability of the results and how these results may be applicable to individual patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.478
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.190
GPT teacher head0.495
Teacher spread0.305 · 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 teacher head, not a consensus.

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

Citations33
Published2007
Admission routes2
Has abstractyes

Explore more

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