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Record W2611701585

Antiprothrombin antibodies detected in two different assay systems: Prevalence and clinical significance in systemic lupus erythematosus

2004· article· en· W2611701585 on OpenAlexaff
María Laura Bertolaccini, Tatsuya Atsumi, T Koike, G R Hughes, Munther A. Khamashta

Bibliographic record

VenueResearch Portal (King's College London) · 2004
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsMedicineThrombosisAntibodyClinical significanceImmunologyVenous thrombosisInternal medicineGastroenterology
DOInot available

Abstract

fetched live from OpenAlex

We evaluated the clinical significance of aPT and aPS-PT by testing for the presence of these antibodies in 212 SLE patients and in 100 healthy individuals. Results show that anti-prothrombin antibodies were found in 47% of the patients (aPT in 31% and aPS-PT in 31%). Their presence did not correlate with that of aCL, anti-beta2GPI, LA and/or anti-protein S. IgG but not IgM aPT were more frequently found in patients with thrombosis than in those without. IgG and IgM aPS-PT were also more frequent in patients with thrombosis (venous and/or arterial) than in those without. Levels of IgG aPT and IgG and IgM aPS-PT were higher in patients with thrombosis than in those without. Although aPT and aPS-PT were more frequently found in women with adverse obstetric history than in those without, the differences were not statistically significant. More significantly, 48% of the patients with aPL-related clinical features who were negative for standard tests had antiprothrombin antibodies. We can conclude that aPT and aPS-PT are frequently found in SLE. Their presence is associated with thrombosis, making these antibodies potential markers for the APS. Testing for these antibodies could be of clinical benefit in patients who are negative for the routinely used tests.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.053
GPT teacher head0.391
Teacher spread0.339 · 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 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

Citations22
Published2004
Admission routes1
Has abstractyes

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