Relationship between Systolic Ejection Time and Inflammation in End-Stage Heart Failure
Bibliographic record
Abstract
OBJECTIVES: Systolic ejection time (SET) and systemic inflammation are two essential indicators of heart failure (HF) progression. We aimed to evaluate the associations between SET and inflammatory mediators in end-stage HF. METHODS: Participants included 16 patients with end-stage HF recruited from the Heart Failure Clinic at Toronto General Hospital and 16 healthy individuals free of any known cardiovascular disease. SET, end systolic pressure, and levels of inflammatory mediators were documented for each patient, and a Spearman rank correlation coefficient was performed to examine differences between patients with end-stage HF and healthy controls. RESULTS: < 0.001) levels were negatively correlated with SET. The levels of other inflammatory mediators-granulocyte-stimulating factor, granulocyte-macrophage colony-stimulating factor, interleukin-8, macrophage inflammatory protein-1, macrophage inflammatory protein-1α, and tumor necrosis factor α-were not significantly correlated with SET. CONCLUSIONS: We found that SET was significantly lower in patients with end-stage HF compared with healthy controls and that reduced SET correlated with increased levels of several inflammatory mediators in patients with HF. By better understanding the relationship between SET and inflammation in HF, a more thorough evaluation could lead to improved risk stratification among patients with HF. Future work should investigate the roles of SET and inflammation in HF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".