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Effect of Docosahexaenoic Acid Supplementation vs Placebo on Developmental Outcomes of Toddlers Born Preterm

2018· article· en· W2898483775 on OpenAlexaff
Sarah A. Keim, Kelly M. Boone, Mark A. Klebanoff, Abigail Norris Turner, Joseph R. Rausch, Mary Ann Nelin, Lynette K. Rogers, Keith Owen Yeates, Leif D. Nelin, Kelly W. Sheppard

Bibliographic record

VenueJAMA Pediatrics · 2018
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsHotchkiss Brain InstituteAlberta Children's HospitalUniversity of Calgary
FundersNational Center for Advancing Translational SciencesNationwide Children's Hospital
KeywordsMedicineToddlerPediatricsBayley Scales of Infant DevelopmentRandomized controlled trialPlaceboDocosahexaenoic acidGestational ageGestationPsychological interventionInternal medicinePolyunsaturated fatty acidCognitionPsychomotor learningPregnancyFatty acidPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

Importance: Intake of dietary docosahexaenoic acid (DHA) among toddlers is low. Supplementation may benefit developmental outcomes of toddlers who were born preterm. Objective: To determine whether 6 months of daily DHA supplementation improves developmental outcomes of toddlers who were born preterm. Design, Setting, and Participants: A randomized, fully masked, placebo-controlled trial was conducted from April 26, 2012, to March 24, 2017, at a large US pediatric academic center with 9 neonatal intensive care units. Children born at less than 35 weeks' gestation who were 10 to 16 months corrected age underwent 6 months of intervention. Of 2363 children assessed, 982 were eligible, 605 declined, and 377 enrolled and were randomized. Analyses were according to intent to treat. Interventions: One-to-one allocation to receive daily microencapsulated DHA, 200 mg, and arachidonic acid (AA), 200 mg (DHA+AA), or microencapsulated corn oil (placebo). Main Outcomes and Measures: The primary outcome specified a priori was Bayley Scales of Infant and Toddler Development, third edition (Bayley-III), cognitive composite score at 16 to 22 months corrected age. Secondary outcomes were Bayley-III language and motor composite scores and Infant Behavior Questionnaire-Revised and Early Childhood Behavior Questionnaire effortful control and activity level scores. Subgroup analyses defined a priori were by income, sex, and birth weight. Results: Among 377 children randomized and included in the analysis (182 girls and 195 boys; median corrected age, 15.7 months), 338 children (89.7%) had complete data on the primary outcome. Bayley-III cognitive scores did not differ between the DHA+AA and placebo groups (difference in change, 0.5 [95% CI, -1.8 to 2.8]; effect size, 0.05; P = .66). Assignment to the DHA+AA group had a small to medium negative effect on Bayley-III language scores among children with lower birth weights (eg, a child with a birth weight of 1000 g assigned to receive DHA+AA experienced a 4.1-point relative decrease, while a child assigned to placebo did not; P = .03 for interaction). Supplementation had a similar negative effect on effortful control scores among children with annual household incomes greater than $35 000 (difference in change, -0.3 [95% CI, -0.4 to -0.1]; effect size, -0.37; P = .01). Bayley-III motor scores and activity level scores were unaffected. Conclusions and Relevance: Daily supplementation with 200 mg of DHA and 200 mg of AA for 6 months resulted in no improvement in cognitive development and early measures of executive function vs placebo, and may have resulted in negative effects on language development and effortful control in certain subgroups of children. These findings do not support DHA supplementation in the second year of life for children who are born preterm. Trial Registration: ClinicalTrials.gov Identifier: NCT01576783.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
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 head0.017
GPT teacher head0.328
Teacher spread0.311 · 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 designRandomized trial
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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Citations45
Published2018
Admission routes1
Has abstractyes

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