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Record W4408851738 · doi:10.1016/j.appet.2025.107980

Outcome measurement instruments used to measure diet-related outcomes in infancy: A scoping review

2025· review· en· W4408851738 on OpenAlexaff
Karen Matvienko‐Sikar, Moira Duffy, Eibhlín Looney, Reindolf Anokye, Catherine S. Birken, Vicki Brown, Darren Dahly, Ann Sinéad Doherty, Dimity Dutch, Rebecca K. Golley, Brittany J. Johnson, Patricia Leahy‐Warren, Marian McBride, Elizabeth McCarthy, Andrew W. Murphy, Sarah Redsell, Caroline B. Terwee

Bibliographic record

VenueAppetite · 2025
Typereview
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsSickKids FoundationUniversity of Toronto
FundersHealth Research BoardHospital Research Foundation
KeywordsMeasure (data warehouse)PsychologyDevelopmental psychologyMedicineComputer scienceData mining

Abstract

fetched live from OpenAlex

INTRODUCTION: Supporting positive diet behaviours during infancy is essential to support child health and prevent childhood obesity. How infant diet-related outcomes are measured in trials is crucial to determining intervention effectiveness. This scoping review examined what and how outcome measurement instruments are currently used to measure 13 infant diet-related outcomes from a previously developed core outcome set. METHODS: The databases EMBASE, MEDLINE, CINAHL and PsycINFO were searched from inception to September 2023. Eligible studies reported trials that included infants ≤1 year old and at least one diet-related outcome measurement instrument. Titles/abstracts and full texts were independently screened in duplicate. Data were narratively synthesised. RESULTS: 136 studies reporting 133 trials were included. Outcome measurement instruments used included 66 questionnaires (n = 70 studies), 65 individual questions (n = 45 studies), 24 food diaries/records (n = 21 studies), 11 24-hour dietary recall (n = 11 studies), and healthcare record data (n = 6 studies). Outcome measurement instruments were predominantly self-administered by researchers in participants homes. There was a lack of reporting for some outcome measurements used. CONCLUSION: Review findings highlight the need to improve clarity and completeness of outcome reporting. The findings also provide an important first step to address heterogeneity in measurement of infant diet-related outcomes. Consistent measurement of diet-related outcomes is needed to improve synthesis and evaluation of obesity prevention interventions.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.348
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.121
GPT teacher head0.403
Teacher spread0.281 · 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.

Study designSystematic review
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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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