High sensitive troponin-I in patients with slow coronary flow pattern
Bibliographic record
Abstract
HYPOTHESIS: We examined the hypothesis that a specific myocardial injury marker, namely high sensitive cardiac troponin-I (HsTn-I), is elevated in patients with slow coronary flow (SCF) pattern. AIM: To examine the above hypothesis by studying a group of patients who had undergone coronary angiography for the detection of their chest pain aetiology with SCF pattern despite an angiographically normal coronary arteriogram. METHODS: We evaluated and performed coronary angiography (CAG) of 97 patients with chest discomfort. The indication forCAG was at least Canada class 3 angina and/or proven myocardial ischaemia according to noninvasive diagnostic tests. We further divided patients into three subgroups according to CAG images and compared HsTn-I plasma levels in 39 patients with SCF pattern, 28 patients with coronary artery disease (CAD), and 30 patients with normal coronary arteries. We researched the association between qualitative HsTn-I positivity and demographic features including cardiovascular risk factors, inflammation markers and TIMI frame count for each of the epicardial coronary arteries. RESULTS: TIMI frame count for each epicardial coronary artery was significantly higher in patients with SCF pattern than in patients with CAD and normal coronary arteries (p < 0.001). HsTn-I positivity was not statistically different between patients with SCF pattern and normal coronary arteries (p = 512), but it was significantly higher in the CAD group than the other two group of patients (p < 0.001). CONCLUSIONS: In patients with SCF, HsTn-I may be detectable, but it is not elevated as in patients with normal coronary arteries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".