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Record W2951348097 · doi:10.1177/2333794x19858526

Biopsychosocial Factors in Children Referred With Failure to Thrive: Modern Characterization for Multidisciplinary Care

2019· article· en· W2951348097 on OpenAlexafffund
Nina Mazze, Emma Cory, Julie Meeks Gardner, Mara Alexanian‐Farr, Carly Mutch, Sherna Marcus, Julie Johnstone, Meta van den Heuvel

Bibliographic record

VenueGlobal Pediatric Health · 2019
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick Children
KeywordsBiopsychosocial modelMedicineFailure to thriveMultidisciplinary approachIntensive care medicineFamily medicinePsychiatryPediatrics

Abstract

fetched live from OpenAlex

The objective of this study was to characterize biopsychosocial characteristics in children with failure to thrive with a focus on 4 domains: medical, nutrition, feeding skills, and psychosocial characteristics. A retrospective cross-sectional chart review was conducted of children assessed at the Infant and Toddler Growth and Feeding Clinic from 2015 to 2016. Descriptive statistics were used to analyze the data. One hundred thirty-eight children, 53.6% male, mean age 16.9 months (SD = 10.8), were included. Approximately one quarter of the children had complex medical conditions, medical comorbidities, and developmental delays. The mean weight-for-age percentile was 15.5 (SD = 23.9), and mean weight-for-length z score was −1.51 (SD = 1.4). A total of 22.5% of children had delayed oral-motor skills and 28.3% had oral aversion symptoms. Caregiver feeding strategies included force feeding (14.5%) and the use of distractions (47.1%). The multifactorial assessment of failure to thrive according to the 4 domains allowed for a better understanding of contributing factors and could facilitate multidisciplinary 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)0.000
metaresearch head score (Gemma)0.000
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 score0.005
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.314
Teacher spread0.302 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

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