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Record W2774814326 · doi:10.1177/1040638717747070

Analytical validation of cardiac troponin I assays in horses

2017· article· en· W2774814326 on OpenAlexaff
Tanya Rossi, Peter A. Kavsak, M. G. Maxie, David L. Pearl, W. Glen Pyle, Peter W. Physick‐Sheard

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsMcMaster UniversityUniversity of GuelphCanadian Animal Health Institute
Fundersnot available
KeywordsTroponinTroponin IMedicineCardiologyInternal medicineVirologyMyocardial infarction

Abstract

fetched live from OpenAlex

Human cardiac troponin I (cTnI) assays have been used in equine medicine, often without prior analytical validation for equine use. In the absence of appropriate validation, the clinical significance of assay results is uncertain and can lead to misdiagnosis. We followed the American Society for Veterinary Clinical Pathology guidelines and investigated linearity, precision, limit of quantification (LoQ), and comparative recovery for 6 commercial cTnI assays developed for use in human medicine. Clinically acceptable linearity was observed in assays A-D, whereas assay E did not detect equine cTnI in any sample. Comparative recovery revealed 1-3-fold differences between assay results, and low analyte recoveries (2.2-3.4%) were observed in assay F. Precision was investigated in assays A and B, and found to be within acceptable limits. The LoQ was 1.53 ng/L for assay A, and 0.031 µg/L for assay B. Assays A and B performed within clinically acceptable limits and were deemed suitable for use in equine medicine. Assays C and D did not undergo full validation but had acceptable linearity, which demonstrates their potential for use in equine medicine. Assays E and F are unsuitable for use in horses given issues with detection of equine cTnI. The variability in results between assays indicates that reference intervals and cutoffs for diagnostic decision-making are assay specific and should be established prior to adoption by diagnostic laboratories.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.077
GPT teacher head0.373
Teacher spread0.296 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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