AME evidence series 001—The Society for Translational Medicine: clinical practice guidelines for diagnosis and early identification of sepsis in the hospital
Bibliographic record
Abstract
Sepsis is a heterogeneous disease caused by an infection stimulus that triggers several complex local and systemic immuno-inflammatory reactions, which results in multiple organ dysfunction and significant morbidity and mortality. The diagnosis of sepsis is challenging because there is no gold standard for diagnosis. As a result, the clinical diagnosis of sepsis is ever changing to meet the clinical and research requirements. Moreover, although there are many novel biomarkers and screening tools for predicting the risk of sepsis, the diagnostic performance and effectiveness of these measures are less than satisfactory, and there is insufficient evidence to recommend clinical use of these new techniques. As a consequence, diagnostic criteria for sepsis need regular revision to cope with emerging evidence. This review aims to present the most updated information on diagnosis and early recognition of sepsis. Recommendations for clinical use of different diagnostic tools rely on the Grades of Recommendation Assessment, Development and Evaluation (GRADE) framework. Because most of the studies were observational and did not allow a reliable assessment of these tools, a two-step inference approach was employed. Future trials need to confirm or refute a particular index test and should directly explore relevant patient outcome parameters.
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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".