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Record W2805022355 · doi:10.1093/pch/pxy054.108

FOOD FOR THOUGHT: BIOPSYCHOSOCIAL FACTORS AND FEEDING BEHAVIOURS IN FAILURE TO THRIVE

2018· article· en· W2805022355 on OpenAlexaff
Nina Mazze, Emma Cory, Julie Meeks Gardner, Mara Alexanian‐Farr, Carly Mutch, Sherna Marcus, Julie Johnstone, Meta van den Heuvel

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsFailure to thriveMedicineBiopsychosocial modelGrowth chartPediatricsReferralWeight for AgeAnthropometryPopulationUnderweightFamily medicinePsychiatryOverweightBody mass indexEnvironmental health

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Failure to thrive (FTT) is prevalent in 5% of the paediatric population and results from the interactions between the child’s health, behaviour, development and social environment. A multi-disciplinary team approach to treat FTT is effective but resources are not always available. OBJECTIVES To characterize biopsychosocial factors and feeding behaviours in children presenting with failure to thrive in our clinic. DESIGN/METHODS A retrospective cross-sectional chart review of children referred to our academic growth and feeding clinic was performed. Children between the ages of 2 months and 5 years with a first clinic visit between 1st January 2015 and 31st of December 2016 were included. Data from the patient’s first visit was included in the study. In a REDCAP database, anthropometric measures according to WHO growth curves, medical history and concurrent developmental delays were recorded. Factors important to the child’s social environment (e.g. maternal mental health, financial problems) were identified. These factors were self-reported by parents to the clinic team or noted on the patient’s referral. Specific attention was paid to the identification of feeding behaviours of children (e.g. vomiting, gagging) and parents (e.g. force feeding, use of distractions). Descriptive statistics were used to analyze the data. RESULTS The study included n = 138 (53.6% male) children with a mean age of 16.9 (SD 10.8) months. The mean weight-for-age percentile was 16.0 (SD 24.3), mean height-for age percentile was 23.8 (SD 30.7), and mean weight-for-length percentile was 16.8 (SD 23.4). 88 (63.8%) children had both growth and feeding behaviour concerns. 26 (18.8%) children were born prematurely and 24 (17.4%) were small for gestational age. 57 (41.3%) children had a history of gastro-oesophageal reflux. In 10 (7.2 %) children, a genetic diagnosis was identified. Concurrent developmental delays were described in the gross motor (20.3%), fine motor (8.0%), speech and language (20.3%) and social domains (6.5%). Feeding developmental milestones that were delayed included not-self feeding (17.4%) and a diet inappropriate for age (20.3%). Important factors that were identified in the child’s social environment were: maternal depression (5.1%), CAS involvement (10.1%) and financial problems (7.2 %). Maternal anxiety was reported but difficult to define. In more than half (50.7%) of the children, feeding behaviours of vomiting, gagging and/or crying and arching were described. Parents used force feeding (14.5%) and distractions (47.1%), and reported mealtimes longer than 30 minutes (70.3%). Most commonly used distractions were television (25.4%) and mobile screens (14.5%). CONCLUSION In our academic population of children with FTT, there is a high incidence of concurrent developmental delays, delayed feeding milestones and feeding behaviour problems. Almost half of the parents used distractions and even more parents prolonged mealtimes to make their child eat. These results underscore the importance of a multi-disciplinary team approach to address feeding behaviours and child development in our population of children with FTT.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.326
Teacher spread0.297 · 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 designNot applicable
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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