Novel high-sensitivity troponin assay requires higher cut-off value to separate acute myocardial infarction from non-acute myocardial infarction in a high-risk population
Bibliographic record
Abstract
PURPOSE: The novel high-sensitivity troponin T assay (hs-cTnT) has been validated for diagnosing AMI in the emergency room. However its utility in high-risk in-patient populations is unknown. METHODS: We retrospectively reviewed admissions to a general cardiology unit that had 2 hs-cTnT measurements in the first 12 h of presentation. We assessed 8 diagnostic algorithms that used hs-cTnT concentration and changes in concentration (including the 99th percentile cut-off of 14 ng/L) for their diagnostic utility in separating AMI patients from cardiac/nonACS and non-cardiac chest-pain patients. UA was excluded. RESULTS: There were 233 patients (mean age 67 years, 153 were males (66%)) admitted over a 2 month period, with AMI diagnosed in 118 of these patients (51%). The recommended 99th percentile cut-off had modest accuracy (65%), good sensitivity (88%), and poor specificity (25%); a higher cut-off of 75 ng/L had a better diagnostic accuracy of 73%, p < 0.05. While some hs-cTnT algorithms were either highly sensitive or specific, none were both. CONCLUSION: In high-risk cardiology in-patients, no hs-cTnT concentration cut-off or change more accurately diagnosed and excluded AMI, although higher cut-offs had better diagnostic utility.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".