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Record W1980851434 · doi:10.1097/mcg.0b013e318230928b

Recommendations for Probiotic Use—2011 Update

2011· article· en· W1980851434 on OpenAlexafffund
Martin H. Floch, W. Allan Walker, Karen Madsen, Mary Ellen Sanders, G.T. Macfarlane, Harry J. Flint, Levinus A. Dieleman, Yehuda Ringel, Stefano Guandalini, Ciarán P. Kelly, Lawrence J. Brandt

Bibliographic record

VenueJournal of Clinical Gastroenterology · 2011
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineIrritable bowel syndromeProbioticDiarrheaMicroecologyNecrotizing enterocolitisClostridium difficileEnterocolitisIntensive care medicineInflammatory bowel diseaseDiseaseImmunologyGastroenterologyInternal medicineMicrobiologyAntibiotics

Abstract

fetched live from OpenAlex

This study describes the consensus opinion of the participants of the third Yale Workshop on probiotic use. There were 10 experts participating. The recommendations update those of the first 2 meetings that were published in 2005 and 2008. The workshop presentations and papers in this supplement relate to the involvement of normal microbiota involved in intestinal microecology, how the microbes interact with the intestine to affect our immunologic responses, the stability and natural history of probiotic organisms, and the role of the intestinal microbatome with regard to affecting cardiac risk factors and obesity. Recommendations for the use of probiotics in necrotizing enterocolitis, childhood diarrhea, inflammatory bowel disease, irritable bowel syndrome, and Clostridium difficile diarrhea are reviewed. As in previous publications, the recommendations are given as A, B, or C ratings. The recent positive experiences with bacteriotherapy (fecal microbiome transplant) are also discussed in detail and a positive recommendation is made for use in severe resistant C. difficile diarrhea.

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)0.002
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 score0.104
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.248
GPT teacher head0.401
Teacher spread0.153 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations204
Published2011
Admission routes2
Has abstractyes

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