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Record W2153713718 · doi:10.1002/ebch.818

<i>The Cochrane Library</i> and the Treatment of Chronic Abdominal Pain in Children and Adolescents: An Overview of Reviews

2011· article· en· W2153713718 on OpenAlexaff
Michelle Foisy, Samina Ali, Rose Geist, Michael Weinstein, Sonia Michail, Kalpesh Thakkar

Bibliographic record

VenueEvidence-Based Child Health A Cochrane Review Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoCochraneUniversity of Alberta
Fundersnot available
KeywordsMedicineSystematic reviewBiofeedbackChronic painAbdominal painPhysical therapyPsychological interventionCochrane LibraryMEDLINEMeta-analysisPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Chronic abdominal pain is a common, disabling and longstanding condition for children and their families that often involves lengthy treatment plans and regular re‐evaluation. Chronic abdominal pain refers to both organic and functional pain, although for the majority of children and adolescents, the pain is functional in origin. Clinicians often feel pressure from parents to provide some type of treatment, and a large number of interventions are used in an attempt to decrease associated symptoms and functional impairment. Substantial variation in the treatment of childhood chronic abdominal pain currently exists. Objectives This overview of reviews aims to synthesize evidence from the Cochrane Database of Systematic Reviews (CDSR) on the efficacy and safety of various dietary, pharmacological and psychological interventions for the treatment of chronic abdominal pain in children and adolescents. Methods Issue 5, 2011 of the CDSR was searched for all reviews examining pharmacologic or non‐pharmacologic treatments for chronic abdominal pain in children and adolescents. All relevant systematic reviews were included, and data were extracted, compiled into tables and synthesized using qualitative and quantitative methods. Main Results Five reviews containing 19 pediatric trials and 777 participants were included in this overview. In one small trial ( n = 64), post‐treatment pain scores and school absences decreased significantly when children received combined cognitive behavioural therapy (CBT), biofeedback, parental support and fibre compared to fibre alone. In two small trials ( n = 47; n = 69), internet CBT compared to standard pediatric care significantly decreased pain at follow‐up and family CBT versus standard care significantly decreased school absences. In adolescents, two small trials ( n = 33; n = 90) found that treatment with amitriptyline compared to placebo had a limited effect on some, but not all, measured outcomes. Data on adverse events were generally lacking for all dietary and psychological interventions; for pharmacological interventions, no differences in adverse events were reported, with only minor adverse events (i.e. fatigue, rash, headache) reported. Authors' Conclusions The successful management of chronic abdominal pain requires a comprehensive, multifaceted approach. The current evidence, albeit limited, suggests that CBT may be effective and that refocusing the patient and family on coping strategies as well as emphasizing normal childhood functioning can have positive results. As such, psychological treatments such as CBT look promising for the treatment of childhood chronic abdominal pain. If pharmacological interventions are considered, current evidence may support a trial of amitriptyline for adolescents with irritable bowel syndrome. However, both of these conclusions are based on a small number of short‐duration trials that are at high or unclear risk of bias. Overall, conclusive evidence is lacking to support dietary, pharmacological and psychological interventions. Future high‐quality, adequately powered trials are eagerly awaited. Copyright © 2011 The Cochrane Collaboration. Published by John Wiley &amp; Sons, Ltd. The Cochrane Collaboration

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.846
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.051
GPT teacher head0.354
Teacher spread0.303 · 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 designOther design
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".

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Citations3
Published2011
Admission routes1
Has abstractyes

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