Troponin assays in the assessment of the equine myocardium
Bibliographic record
Abstract
In 2000, troponin assays were adopted as the test of choice for detection of myocardial injury in man. This decision was made after extensive testing and followed a 60 year search for a biomarker of myocardial damage with sufficient analytical sensitivity and specificity. This has led to proliferation of assays for use in human medicine, each requiring extensive testing and validation before it could be made available on the open market for human use. The search for ever-more analytically sensitive assays and for a standard reference material continues. The adoption of troponin testing in veterinary medicine followed shortly after its development for use in man, providing a much-needed means of detecting and monitoring myocardial damage in horses. However, application of these tests in veterinary medicine has exclusively involved use of assays designed for and clinically validated in human patients. There is no mandated requirement for test validation in veterinary medicine and, while many of these assays have been shown to be capable of detecting equine troponin, the wide diversity of available tests, lack of validation, absence of protocols for their use and lack of standardisation make their application problematic. The objective of this review article is to address this issue, offering guidance where data are available and encouraging caution where there are none. Ultimately, the overall goal of this review is to examine critically the use of troponin assays in the horse and to promote the accurate and appropriate interpretation of valid results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".