Probiotics and prebiotics to combat enteric infections and HIV in the developing world: a consensus report
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
Infectious disease in the developing world continues to represent one of the greatest challenges facing humanity. Every year over a million children suffer and die from the sequela of enteric infections, while in 2008 it is estimated almost 2.7 million (UNAIDS 2009 update) adults and children became infected with human immunodeficiency virus (HIV). While oral rehydration therapy for diarrhea, and antiretrovirals (ARV) for HIV are critical, there is a place for adjunctive therapies to improve quality of life. The importance of the human microbiota in retaining health is now recognized, as is the concept of replenishing beneficial microbes through probiotic treatments. Studies have shown that probiotics can reduce the duration of diarrhea, improve gut barrier function, help prevent bacterial vaginosis (BV), and enhance immunity even in HIV-infected subjects. However, many issues remain before the extent of probiotic benefits can be verified, and their application to the developing world realised. This consensus report outlines the potential probiotic, and to a lesser extent prebiotic, applications in resource disadvantages settings, and recommends steps that could bring tangible relief to millions of people. The challenges to both efficacy and effectiveness studies in these settings include a lack of infrastructure and funding for scientists, students and research projects in developing countries; making available clinically proven probiotic and prebiotic products at affordable prices; and undertaking appropriately designed clinical trials. We present a roadmap on how efficacy studies may be conducted in a resource disadvantages setting among persons with chronic diarrhea and HIV. These examples and the translation of efficacy into effectiveness are described.
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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 it