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Record W2128110558 · doi:10.1086/523340

How Science Will Help Shape Future Clinical Applications of Probiotics

2008· review· en· W2128110558 on OpenAlexaff
Gregor Reid

Bibliographic record

VenueClinical Infectious Diseases · 2008
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsLawson Health Research InstituteWestern University
FundersNational Center for Complementary and Alternative MedicineNational Institutes of HealthCalifornia Dairy Research FoundationGeneral Mills
KeywordsMedicineIsolation (microbiology)DocumentationIntensive care medicineInfectious disease (medical specialty)Clinical PracticeBiotechnologyRisk analysis (engineering)DiseaseBioinformaticsBiologyPathologyComputer science

Abstract

fetched live from OpenAlex

The recent increased interest in probiotics among clinicians has many causes, primarily the concern about the limitations of the current armamentarium of pharmaceutical agents. Although probiotics have been used mostly in dietary supplements and foods to maintain health, scientific and clinical studies are recognizing the potential of some probiotics to be therapeutic in function. Scientific breakthroughs in understanding the source and composition of the human microbiota, the key nutritional factors that influence these microbes, and their immunomodulatory effects; the creation of disease-targeted recombinant strains; the isolation and characterization of signaling molecules that can modulate microbial biofilms and infectious processes; and advances in biomedical engineering that will provide new delivery systems for probiotics will shape the future of clinical applications of probiotics. In time and with rigorous documentation, some probiotics will likely find an important place in medical practice.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0010.001
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.004

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 head0.072
GPT teacher head0.372
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2008
Admission routes1
Has abstractyes

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