The ratio of circulating endothelin-1 to endothelin-3 associated with TIMI risk and dynamic TIMI risk score in ST elevation acute myocardial infarction
Bibliographic record
Abstract
In ST segment elevation acute myocardial infarction (STEMI), the endothelin (ET) system imbalance, reflected by the circulating ET-1:ET-3 ratio has not been investigated. This study’s primary objective was to measure the circulating ET-1:ET-3 ratio and correlate it with the risk stratification for 1 year mortality of STEMI based on TIMI score. On admission, the TIMI risk score and at discharge, the dynamic TIMI risk score were calculated in 68 consecutive subjects with STEMI. Subjects with high TIMI risk score were associated with higher mean ET-1 level and ET-1:ET-3 ratio. The ET-1:ET-3 ratio more accurately predicted the high on admission TIMI risk score than the ET-1 level. Subjects with high dynamic TIMI risk score were associated with higher mean ET-1 level and ET-1:ET-3 ratio. The ET-1:ET-3 ratio more accurately predicted the high at discharge dynamic TIMI risk score than ET-1 level. From multivariable analysis, the ET-1:ET-3 ratio was not independently associated with high on admission TIMI risk score but independently predicted high at discharge dynamic TIMI risk score (odds ratio = 9.186, p = 0.018). In conclusion, combining the ET-1 and ET-3 levels into the ET-1:ET-3 ratio provided a prognostic value by independently predicting the increased risk to 1 year mortality as indicated by at discharge dynamic TIMI risk score in patients with STEMI.
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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 | 0.003 |
| 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 | 0.000 | 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 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".