In Search of a Cure for Sepsis: Taming the Monster in Critical Care Medicine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In spite of over half a century of research, sepsis still constitutes a major problem in health care delivery. Although advances in research have significantly increased our knowledge of the pathogenesis of sepsis and resulted in better prognosis and improved survival outcome, sepsis still remains a major challenge in modern medicine with an increase in occurrence predicted and a huge socioeconomic burden. It is generally accepted that sepsis is due to an initial hyperinflammatory response. However, numerous efforts aimed at targeting the proinflammatory cytokine network have been largely unsuccessful and the search for novel potential therapeutic targets continues. Recent studies provide compelling evidence that dysregulated anti-inflammatory responses may also contribute to sepsis mortality. Our previous studies on the role of regulatory T cells and phosphoinositide 3-kinases in sepsis highlight immunological approaches that could be explored for sepsis therapy. In this article, we review the current and emerging concepts in sepsis, highlight novel potential therapeutic targets and immunological approaches for sepsis treatment and propose a biphasic treatment approach for management of the condition.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it