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Record W2087332309 · doi:10.1309/ajcp7ybj6urtvcwp

Comparison of Russell Viper Venom–Based and Activated Partial Thromboplastin Time–Based Screening Assays for Resistance to Activated Protein C

2008· article· en· W2087332309 on OpenAlexaboutno aff
A. Zara Herskovits, Susan J. Lemire, Janina A. Longtine, David M. Dorfman

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2008
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsPartial thromboplastin timeActivated protein C resistanceViper VenomsMedicineSnake venomFactor V LeidenThromboplastinImmunologyProtein CInternal medicineMolecular biologyCoagulationVenomBiologyThrombosisBiochemistry

Abstract

fetched live from OpenAlex

Thrombotic disease is a significant cause of mortality and morbidity, with an estimated lifetime risk of greater than 10% in Western populations. One of the most common hereditary thrombophilias is the factor V Leiden mutation, which is identified with a screening assay for activated protein C (APC) resistance and confirmed by DNA analysis. In this study, we compared the commercially available Pefakit (Pentapharm, Basel, Switzerland) and Cryocheck (Precision BioLogic, Dartmouth, Canada) assays, 2 recently developed Russell viper venom (RVV)-based screening tests, with the activated partial thromboplastin time (aPTT)-based screening test currently used in our hospital's clinical laboratory. We found that the aPTT-based assay for resistance to APC had a sensitivity of 100%, a specificity of 70%, and a positive predictive value (PPV) of 70%, whereas both of the RVV-based assays exhibited high sensitivity, specificity, and PPV at 100%. In addition, we found that these new functional assays are more cost-effective relative to the screening algorithm previously used in our clinical laboratory and could potentially eliminate the need for DNA analysis, although further study is required.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.104
GPT teacher head0.412
Teacher spread0.308 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2008
Admission routes1
Has abstractyes

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