Can prebiotics and probiotics improve therapeutic outcomes for undernourished individuals?
Bibliographic record
Abstract
It has become clear in recent years that the human intestinal microbiota plays an important role in maintaining health and thus is an attractive target for clinical interventions. Scientists and clinicians have become increasingly interested in assessing the ability of probiotics and prebiotics to enhance the nutritional status of malnourished children, pregnant women, the elderly, and individuals with non-communicable disease-associated malnutrition. A workshop was held by the International Scientific Association for Probiotics and Prebiotics (ISAPP), drawing on the knowledge of experts from industry, medicine, and academia, with the objective to assess the status of our understanding of the link between the microbiome and under-nutrition, specifically in relation to probiotic and prebiotic treatments for under-nourished individuals. These discussions led to four recommendations: (1) The categories of malnourished individuals need to be differentiated To improve treatment outcomes, subjects should first be categorized based on the cause of malnutrition, additional health-concerns, differences in the gut microbiota, and sociological considerations. (2) Define a baseline "healthy" gut microbiota for each category Altered nutrient requirement (for example, in pregnancy and old age) and individual variation may change what constitutes a healthy gut microbiota for the individual. (3) Perform studies using model systems to test the effectiveness of potential probiotics and prebiotics against these specific categories These should illustrate how certain microbiota profiles can be altered, as members of different categories may respond differently to the same treatment. (4) Perform robust well-designed human studies with probiotics and/or prebiotics, with appropriate, defined primary outcomes and sample size These are critical to show efficacy and understand responder and non-responder outcomes. It is hoped that these recommendations will lead to new approaches that combat malnutrition. This report is the result of discussion during an expert workshop titled "How do the microbiota and probiotics and/or prebiotics influence poor nutritional status?" held during the 10th Meeting of the International Scientific Association for Probiotics and Prebiotics (ISAPP) in Cork, Ireland from October 1-3, 2012. The complete list of workshop attendees is shown in Table 1.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".