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Synbiotics for Improved Human Health: Recent Developments, Challenges, and Opportunities

2018· review· en· W2619400681 on OpenAlexaff
Janina A. Krumbeck, Jens Walter, Robert W. Hutkins

Bibliographic record

VenueAnnual Review of Food Science and Technology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Alberta
FundersEuropean CommissionEuropean Food Safety AuthorityU.S. Department of Agriculture
KeywordsSynbioticsHuman healthHealth benefitsHuman studiesManagement scienceMedicineBiotechnologyRisk analysis (engineering)BiologyEnvironmental healthEngineeringProbioticTraditional medicine

Abstract

fetched live from OpenAlex

Research on combining pro- and prebiotics as synbiotics to enhance human and animal health has accelerated in the past 10 years, including many clinical trials that have assessed a diverse range of synbiotic formulations. In this review, we summarize these studies as well as the commercial applications of synbiotics that are available. In particular, we critically assess the claimed health benefits of synbiotic applications and the ecological and therapeutic factors to consider when designing synbiotics and discuss the implications of these concepts for future research in this field.

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.

How this classification was reachedexpand

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.112
GPT teacher head0.383
Teacher spread0.272 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations79
Published2018
Admission routes1
Has abstractyes

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