Simulation Models of Misclassification Error for Single Thresholds of High-Sensitivity Cardiac Troponin I Due to Assay Bias and Imprecision
Bibliographic record
Abstract
BACKGROUND: Clinical outcome studies for cardiac troponins (cTn) are expensive and difficult to design owing to variation in patients, in the assays, and in the incidence of different types of myocardial infarction (MI). To overcome these difficulties, simulation models were used to estimate the rate of misclassification error for MI and risk prediction resulting from assay bias and imprecision. METHODS: Finite mixture analysis of Abbott high-sensitivity cTnI (hs-cTnI) results at time 0 h in patients presenting early with acute coronary syndrome (ACS) symptoms to the emergency department (ED) [n = 145, Reducing the Time Interval for Identifying New Guideline (RING) study] allowed derivation of a simulation data set (n = 10000). hs-cTnI concentrations were modified by addition of bias or imprecision error. The percentage of all 10000 modified hs-cTnI results that were misclassified for MI at thresholds of 2, 5, 26.2, and 52 ng/L was determined by Monte Carlo simulation. Analyses were replicated with an all-comer emergency department (ED) population (n = 1137) ROMI (Optimum Troponin Cutoffs for ACS in the ED) study. RESULTS: In the RING study, simulation at 26.2-ng/L (99th percentile) and 52-ng/L thresholds were affected by both bias ±2 ng/L and imprecision (10%-20%) and had misclassification rates of 0.4% to 0.6%. Simulations at the 2-ng/L and 5-ng/L thresholds were only affected by bias. Misclassification rates at bias of ±1 ng/L were 10% for the 2-ng/L threshold, and 5% for the 5-ng/L threshold. CONCLUSIONS: Simulation models predicted that hs-cTnI results are seldom misclassified (<1% of patients) when interpretative thresholds are near or exceed the overall 99th percentile. However, simulation models also predicted that low hs-cTnI results, as recommended in guidelines, are prone to misclassification of 5%-10% of patients.
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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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".