Current challenges in the management of sepsis in ICUs in resource-poor settings and suggestions for the future
Bibliographic record
Abstract
Sepsis is a major reason for intensive care unit (ICU) admission, also in resource-poor settings. ICUs in low- and middle-income countries (LMICs) face many challenges that could affect patient outcome. To describe differences between resource-poor and resource-rich settings regarding the epidemiology, pathophysiology, economics and research aspects of sepsis. We restricted this manuscript to the ICU setting even knowing that many sepsis patients in LMICs are treated outside an ICU. Although many bacterial pathogens causing sepsis in LMICs are similar to those in high-income countries, resistance patterns to antimicrobial drugs can be very different; in addition, causes of sepsis in LMICs often include tropical diseases in which direct damaging effects of pathogens and their products can sometimes be more important than the response of the host. There are substantial and persisting differences in ICU capacities around the world; not surprisingly the lowest capacities are found in LMICs, but with important heterogeneity within individual LMICs. Although many aspects of sepsis management developed in rich countries are applicable in LMICs, implementation requires strong consideration of cost implications and the important differences in resources. Addressing both disease-specific and setting-specific factors is important to improve performance of ICUs in LMICs. Although critical care for severe sepsis is likely cost-effective in LMIC setting, more detailed evaluation at both at a macro- and micro-economy level is necessary. Sepsis management in resource-limited settings is a largely unexplored frontier with important opportunities for research, training, and other initiatives for improvement.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 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".