High‐sensitivity cardiac troponin T as a predictor of acute Total occlusion in patients with non‐ST‐segment elevation acute coronary syndrome
Bibliographic record
Abstract
BACKGROUND: A large percentage of patients with non-ST-segment acute coronary syndrome (NSTE-ACS) present with acute total occlusion (TO) of some major epicardial vessel that does not generate electrocardiographic changes. Ongoing research into the methods of accurately predicting acute TO have not yielded great success. HYPOTHESIS: High-sensitivity cardiac troponin T (hs-cTnT) has a good predictive value for the presence of acute TO of the culprit artery in patients with NSTE-ACS. METHODS: A single-center retrospective study of 1011 patients diagnosed with NSTE-ACS who underwent coronary angiography and hs-cTnT measured on admission. The predictive value of hs-cTnT in the presence of acute TO was assessed by the area under the ROC curve. RESULTS: The mean age of the population was 67.12 ± 13.18 and 74.1% were male. 7.3% of the patients presented with acute TO. The AUC for hs-cTnT to predict acute TO was 0.95. A hs-cTnT value of 1006 ng/L (71.8 fold of the URL) best predicted the presence of acute TO, with a sensitivity of 86% and specificity of 95% positive predictive value (PPV): 86% and negative predictive value (NPV): 94%. CONCLUSIONS: Hs-cTnT was a good predictor of acute TO in patients with NSTE-ACS. Hs-cTnT values greater than 1006 ng/L were highly predictive of acute TO of a major coronary vessel.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".