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Quality of Life of Infants with Functional Gastrointestinal Disorders: A Large Prospective Observational Study

2017· article· en· W2619431784 on OpenAlexvenueno aff
Camille Jung, Thierry Hanh, M. Bellaïche

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInfant Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObservational studyQuality of life (healthcare)Intensive care medicinePediatricsInternal medicineNursing

Abstract

fetched live from OpenAlex

Background: Functional gastrointestinal disorders (FGID) are very common during infancy, leading to frequent medical consultations. The aim of this large, prospective study was to assess the quality of life (QoL) and clinical management of infants with FGID. Methods: Completely or partially bottle-fed infants under 5 months old, presenting one or more FGID (regurgitation, constipation, diarrhea, crying/fussing), were enrolled during initial consultation by 111 pediatricians in private practice throughout France and reassessed at one month. Parents were asked to complete the QUALIN QoL questionnaire at inclusion and at Day 15. Results: A total of 815 infants (mean age 2.1±1.2 months) were evaluable. Mean QoL score improved from +27.2±15.1 at inclusion to +38.0±12.9 at day 15 (p<0.0001) irrespective of FGID symptoms. Multivariate analysis indicated that younger age, dietary advice, and partial breastfeeding were associated with better QoL outcome. Gastrointestinal symptoms showed significant regression at Day 30. The number of bottle feeds followed by external reflux episodes decreased from 80.0±27.4% to 36.1±31.4% at Day 30 (p<0.0001), the weekly number of stools increasing from 3.9±4.0 to 8.0±3.7 (p<0.0001). Conclusion: Medical management based on information, reassurance, lifestyle advice and dietary intervention improved QoL in infants with FGID and led to a reduction in FGID symptoms.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.102
GPT teacher head0.447
Teacher spread0.345 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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