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Record W4307282817 · doi:10.1177/00099228221132337

Describing and Predicting Feeding Problems During the First 2 Years Within an Urban Pediatric Primary Care Center

2022· article· en· W4307282817 on OpenAlexaboutno aff
Angela Caldwell, Lauren Terhorst, Katelin Magnan, Debra L. Bogen

Bibliographic record

VenueClinical Pediatrics · 2022
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsnot available
FundersNational Institute of Child Health and Human DevelopmentNational Institutes of HealthNational Center for Medical Rehabilitation ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentSchool of Health and Rehabilitation Sciences, University of PittsburghUniversity of Pittsburgh
KeywordsMedicineAnxietyReferralPediatricsCohortDescriptive statisticsMealDemographyFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

We conducted a prospective cohort study with children aged 6 to 18 months to identify predictors of feeding problems in an urban sample. Parent-reported child feeding problems (Montreal Children’s Hospital Feeding Scale) and picky eating, parental anxiety (Generalized Anxiety Disorder 7), and family meal structure (Meals in Our Household) were assessed via a Web-based survey at 3 time points. Data analysis included descriptive statistics, correlations, and mixed-effects regression modeling. Eighty parents completed the survey. Child picky eating ( r =.51) and resistance to try new foods ( r = .30), parental anxiety ( r = .34), rushed mealtimes ( r = .28), and child age ( r = .32) were significantly associated with child feeding problems. Feeding Scale scores were, on average, 6 points higher among picky eaters than those who were not over time ( p < .001). Parent reports of picky eating early in life may warrant additional clinical investigation and referral to feeding specialists.

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)0.001
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 score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.053
GPT teacher head0.289
Teacher spread0.236 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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