Deeper understanding of mechanisms contributing to sepsis-induced myocardial dysfunction
Bibliographic record
Abstract
The inflammatory response of sepsis results in organ dysfunction, including myocardial dysfunction. Myocardial dysfunction is particularly important in patients with severe septic shock who progress to a hypodynamic pre-terminal phase. Multiple aspects of this septic inflammatory response contribute to the pathogenesis of decreased ventricular contractility. Inflammatory cytokines released by inflammatory cells contribute as does nitric oxide released by vascular endothelium and by cardiomyocytes. Endotoxins and other pathogen molecules induce an intramyocardial inflammatory response by binding Toll-like receptors on cardiomyocytes that then signal via NF-κB. These processes alter cardiomyocyte depolarization and, therefore, contractility. The particular role of the cardiomyocyte sodium current has not been characterized. Now new information suggests that the septic inflammatory response impairs normal depolarization by altering the cardiomyocyte sodium current. This results in decreased ventricular contractility. This is important because new targets for therapeutic intervention can be considered and new approaches to evaluation of this problem can be contemplated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".