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Record W2979245391 · doi:10.1093/pch/pxz132

Dietary interventions for recurrent abdominal pain in childhood

2019· article· en· W2979245391 on OpenAlexaff
Matthew Carroll

Bibliographic record

VenuePaediatrics & Child Health · 2019
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsStollery Children's HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineFunctional gastrointestinal disorderPlaceboIrritable bowel syndromePsychological interventionAbdominal painMEDLINEPhysical therapyRandomized controlled trialAlternative medicinePediatricsInternal medicinePsychiatryPathology

Abstract

fetched live from OpenAlex

This is an update of the original Cochrane review, last published in 2009 (Huertas-Ceballos 2009). Recurrent abdominal pain (RAP), including children with irritable bowel syndrome, is a common problem affecting between 4% and 25% of school-aged children. For the majority of such children, no organic cause for their pain can be found on physical examination or investigation. Many dietary inventions have been suggested to improve the symptoms of RAP. These may involve either excluding ingredients from the diet or adding supplements such as fibre or probiotics. To examine the effectiveness of dietary interventions in improving pain in children of school age with RAP. We searched CENTRAL, Ovid MEDLINE, Embase, eight other databases, and two trials registers, together with reference checking, citation searching and contact with study authors, in June 2016. Randomised controlled trials (RCTs) comparing dietary interventions with placebo or no treatment in children aged five to 18 years with RAP or an abdominal pain-related, functional gastrointestinal disorder, as defined by the Rome III criteria (Rasquin 2006).

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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 designSystematic review
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

Citations4
Published2019
Admission routes1
Has abstractyes

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