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Record W1967863803 · doi:10.4161/gmic.27252

Can prebiotics and probiotics improve therapeutic outcomes for undernourished individuals?

2013· article· en· W1967863803 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGut Microbes · 2013
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsLawson Health Research Institute
FundersScience Foundation IrelandScottish Government
KeywordsBiologyProbioticIntensive care medicineMedicineGeneticsBacteria

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.264
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it