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Record W2897971695 · doi:10.1515/cclm-2018-0905

Cardiac troponin and natriuretic peptide analytical interferences from hemolysis and biotin: educational aids from the IFCC Committee on Cardiac Biomarkers (IFCC C-CB)

2018· article· en· W2897971695 on OpenAlexaff
Amy K. Saenger, Allan S. Jaffe, Richard Body, Paul Collinson, Peter A. Kavsak, Carolyn S.P. Lam, Guillaume Lefèvre, Tobjørn Omland, Jordi Ordóñez‐Llanos, Kari Pulkki, Fred S. Apple

Bibliographic record

VenueClinical Chemistry and Laboratory Medicine (CCLM) · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsMcMaster University
FundersNational Institute for Health and Care Research
KeywordsHemolysisMedicineTroponin complexInternal medicineIntensive care medicineTroponin IMyocardial infarction

Abstract

fetched live from OpenAlex

Two interferences recently brought to the forefront as patient safety issues include hemolysis (hemoglobin) and biotin (vitamin B7). The International Federation for Clinical Chemistry Committee on Cardiac Biomarkers (IFCC-CB) obtained input from a majority of cTn and NP assay manufacturers to collate information related to high-sensitivity (hs)-cTnI, hs-cTnT, contemporary, and POC cTn assays, and NP assays interferences due to hemolysis and biotin. The information contained in these tables was designed as educational tools to aid laboratory professionals and clinicians in troubleshooting cardiac biomarker analytical results that are discordant with the clinical situation.

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)0.020
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.055
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0160.006

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 head0.029
GPT teacher head0.338
Teacher spread0.309 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations44
Published2018
Admission routes1
Has abstractyes

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