Recommendations for infection management in patients with sepsis and septic shock in resource-limited settings
Bibliographic record
Abstract
Studies indicate that sepsis and septic shock in resource-limited settings are at least as common as in resource-rich settings. The surviving sepsis campaign (SSC) guidelines have been widely adopted throughout the world, but in resource-limited settings are often unfeasible [ 1 ]. The guidelines are based almost exclusively on evidence from resource-rich settings and are not necessarily applicable elsewhere due to differences in etiology and diagnostic or treatment capacity. An international team of physicians with extensive practical experience in resource-limited intensive care units (ICUs) identified key questions concerning the SSC’s infection management recommendations, and evidence from resource-limited settings regarding these was evaluated using the grading of recommendations assessment, development and evaluation (GRADE) tools. This article focuses primarily on bacterial causes of sepsis and septic shock. Other infections common in resource-limited settings, such as malaria, are covered in a separate article in this series. Evidence quality was scored as high (grade A), moderate (B), low (C), or very low (D), and recommendations as strong (1) or weak (2). The major difference from the grading of recommendations in the SSC-guidelines was in taking account of contextual factors relevant to resource-limited settings, such as the availability, affordability and feasibility of interventions in resource-limited ICUs. Strong recommendations have been worded as ‘we recommend’ and weak recommendations as ‘we suggest’ (details in online supplement).
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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 | 0.014 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".