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Record W2801532073 · doi:10.1017/ice.2018.84

Microbial Preparations (Probiotics) for the Prevention of <i>Clostridium difficile</i> Infection in Adults and Children: An Individual Patient Data Meta-analysis of 6,851 Participants

2018· review· en· W2801532073 on OpenAlexaff
Bradley C. Johnston, Lyubov Lytvyn, Calvin Ka-Fung Lo, Stephen Allen, Duolao Wang, Hania Szajewska, Mark A. Miller, Stephan Ehrhardt, John S. Sampalis, Deniz Duman, Pietro Pozzoni, Agostino Colli, Elisabet Lönnermark, Christian P. Selinger, Samford Wong, Susan Plummer, Mary Hickson, Rouzha Pancheva, Sandra Hirsch, Bengt Klarin, Joshua Z. Goldenberg, Li Wang, Lawrence Mbuagbauw, Gary Foster, Anna Maw, Behnam Sadeghirad, Lehana Thabane, Dominik Mertz

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2018
Typereview
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcGill UniversityMcMaster University Medical CentreMcMaster UniversityDalhousie University
FundersAstellas PharmaNational Institute for Health and Care ResearchDanone
KeywordsClostridium difficileMeta-analysisMedicineClostridiumC difficileClostridium InfectionsMicrobiologyInternal medicineBiologyBacteriaAntibiotics

Abstract

fetched live from OpenAlex

OBJECTIVETo determine whether probiotic prophylaxes reduce the odds of Clostridium difficile infection (CDI) in adults and children.DESIGNIndividual participant data (IPD) meta-analysis of randomized controlled trials (RCTs), adjusting for risk factors.METHODSWe searched 6 databases and 11 grey literature sources from inception to April 2016. We identified 32 RCTs (n=8,713); among them, 18 RCTs provided IPD (n=6,851 participants) comparing probiotic prophylaxis to placebo or no treatment (standard care). One reviewer prepared the IPD, and 2 reviewers extracted data, rated study quality, and graded evidence quality.RESULTSProbiotics reduced CDI odds in the unadjusted model (n=6,645; odds ratio [OR] 0.37; 95% confidence interval [CI], 0.25-0.55) and the adjusted model (n=5,074; OR, 0.35; 95% CI, 0.23-0.55). Using 2 or more antibiotics increased the odds of CDI (OR, 2.20; 95% CI, 1.11-4.37), whereas age, sex, hospitalization status, and high-risk antibiotic exposure did not. Adjusted subgroup analyses suggested that, compared to no probiotics, multispecies probiotics were more beneficial than single-species probiotics, as was using probiotics in clinical settings where the CDI risk is ≥5%. Of 18 studies, 14 reported adverse events. In 11 of these 14 studies, the adverse events were retained in the adjusted model. Odds for serious adverse events were similar for both groups in the unadjusted analyses (n=4,990; OR, 1.06; 95% CI, 0.89-1.26) and adjusted analyses (n=4,718; OR, 1.06; 95% CI, 0.89-1.28). Missing outcome data for CDI ranged from 0% to 25.8%. Our analyses were robust to a sensitivity analysis for missingness.CONCLUSIONSModerate quality (ie, certainty) evidence suggests that probiotic prophylaxis may be a useful and safe CDI prevention strategy, particularly among participants taking 2 or more antibiotics and in hospital settings where the risk of CDI is ≥5%.TRIAL REGISTRATIONPROSPERO 2015 identifier: CRD42015015701Infect Control Hosp Epidemiol 2018;771-781.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.188
GPT teacher head0.425
Teacher spread0.237 · 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.

Study designMeta-analysis
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

Citations51
Published2018
Admission routes1
Has abstractyes

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