Cardiovascular Involvement in Sepsis
Bibliographic record
Abstract
This special issue wants to contribute to a better understanding of cardiac involvement in sepsis. Sepsis is a complex syndrome that has recently been defined as “life-threatening organ dysfunction due to a dysregulated host response to infection”. It should be considered a major public health problem since it affects millions of people worldwide each year, and it accounts for most deaths in critically ill patients. The presence of myocardial dysfunction in sepsis is associated with higher mortality. A great attention has been dedicated to improving our knowledge and understanding of the intricate mechanisms underlying sepsis. However, data from the literature suggest the need to implement strategies to reliably measure sepsis morbidity and mortality. In fact, methods based on analyses of insurance claim data using sepsis-specific codes or separate codes for infection and organ dysfunction are unreliable in informing or measuring the effects of policy changes, and the postmortem diagnosis of sepsis is often elusive since postmortem investigations lack certain pathognomonic macroscopic and histopathological findings. From a morphological and diagnostic point of view, the term “septic disease” has been created to describe the cardiac involvement in the syndrome. However, this definition, rather than describing a morphological finding, was instead referred to the clinical setting. Although in recent years the concept of septic cardiomyopathy has evolved and it involves pathological alterations of myocardial cells in response to the multiplicity of acting mechanism of damage, the importance of structural changes during sepsis is often overlooked. In patients with sepsis, death is usually the result of a progressive multiorgan dysfunction, overlooking the primary infection through the hyperinflammation. The cardiac involvement as fundamental part of septic multiorgan dysfunction syndrome has been discussed for a long time.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".