Leveraging the microbiome in the treatment of sepsis: potential pitfalls and new perspectives
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review aims to provide an overview of the current knowledge about microbiota-targeted therapies in sepsis, and calls out - despite recent negative studies - not to halt our efforts of translating these tools into regular medical practice. RECENT FINDINGS: The intestinal microbiome has an important role in shaping our immune system, and microbiota-derived metabolites prime innate and adaptive inflammatory responses to infectious pathogens. Microbiota composition is severely disrupted during sepsis, which has been linked to increased risk of mortality and secondary infections. However, efforts of using these microbes as a tool for prognostic or therapeutic purposes have been unsuccessful so far, and recent trials studying the impact of probiotics in critical illness did not improve patient outcomes. Despite these negative results, researchers must continue their attempts of harnessing the microbiome to improve sepsis survival in patients with a high risk of clinical deterioration. Promising research avenues that could potentially benefit sepsis patients include the development of next-generation probiotics, use of the microbiome as a theranostic tool to direct therapy, and addressing the restoration of microbial communities following ICU discharge. SUMMARY: Although research focused on microbiome-mediated therapy in critically ill patients has not yielded the results that were anticipated, we should not abandon our efforts to translate promising preclinical findings into clinical practice.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".