Determination of Clinically Acceptable Analytical Variation of Cardiac Troponin at Decision Thresholds
Bibliographic record
Abstract
BACKGROUND: Clinical decision-making for risk stratification for possible myocardial infarction (MI) uses high-sensitivity cardiac troponin (hs-cTn) thresholds that range from the limit of detection to several-fold higher than the upper reference limit (URL). To establish a minimum analytical variation standard, we can quantify the effect of variation on the population clinical measures of safety (sensitivity) and effectiveness [proportion below threshold, or positive predictive value (PPV)]. METHODS: From large datasets of patients investigated for possible MI with the Abbott hs-cTnI and Roche hs-cTnT assays, we synthesized datasets of 1 000 000 simulated patients. Troponin concentrations were randomly varied several times based on absolute deviations of 0.5 to 3 ng/L and relative changes of 2% to 20% around the low-risk threshold (5 ng/L) and URLs, respectively. RESULTS: For both assays at the low-risk thresholds, there were negligible differences in sensitivity (<0.3%) with increasing analytical variation. The proportion of patients characterized as low risk reduced by 30% to 29% (Roche) and 53% to 44% (Abbott). At the URL, increasing analytical variation also did not change sensitivity; the PPV fell by less than 3%. For risk stratification, increased delta thresholds (change between serial troponin concentrations) increased sensitivity at the cost of a decreased percentage of patients below the delta threshold, with the largest changes at the greatest analytical variation. CONCLUSIONS: At the low-risk threshold, analytical variation up to 3 ng/L minimally impacted the safety metric (sensitivity) but marginally reduced effectiveness. Similarly, at the URL even relative variation up to 25% minimally impacted safety metrics and effectiveness. Analytical variation for delta thresholds did not negatively impact sensitivity but decreased effectiveness.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".