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Record W2337442037 · doi:10.1309/ajcp8mwu4qstclpu

Protein C Assay Performance

2012· article· en· W2337442037 on OpenAlexaff
Jason M. Baron, Stephen M. Johnson, Marlies Ledford-Kraemer, Catherine P.M. Hayward, Piet Meijer, Elizabeth M. Van Cott

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2012
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChromogenicReagentCoagulationBradford protein assayMedicineChromatographyImmunologyChemistryInternal medicine

Abstract

fetched live from OpenAlex

To determine the performance and frequency of protein C reagents currently used by clinical laboratories, we analyzed North American Specialized Coagulation Laboratory Association (NASCOLA) protein C proficiency testing data from 6 surveys conducted in 2009 and 2010 (2009-1 to 2009-3 and 2010-1 to 2010-3). Interlaboratory coefficients of variation (CV) for commonly used reagents on a survey with normal protein C ranged from 8% to 12% for antigenic assays, from 4% to 7% for chromogenic activity assays, and from 7% to 22% for clot-based activity assays. CVs for commonly used reagents on specimens with abnormal protein C ranged from 15% to 24% for antigenic, 4% to 11% for chromogenic, and 10% to 17% for clot-based assays (averaged across 3 surveys). Some reagents were used by relatively few laboratories and therefore additional study may be needed for those reagents. For all commonly used reagents, biases were usually small and often not statistically significant. All assessed reagents were clinically accurate, and were considered acceptable options for a specialized coagulation laboratory.

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)0.036
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.052
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.071
GPT teacher head0.397
Teacher spread0.325 · 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 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

Citations21
Published2012
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Clinical PathologySame topicBlood Coagulation and Thrombosis MechanismsFrench-language works237,207