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Record W4296160572 · doi:10.1111/dmcn.15381

Early detection of developmental delay in infants born very preterm or with very low birthweight

2022· article· en· W4296160572 on OpenAlexaboutno aff
Rebecca Caesar, Roslyn N. Boyd, Giovanni Cioni, Robert S. Ware, Julie Doherty, Maxine Jackson, Kaye L. Salthouse, Paul B. Colditz

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
FundersMedical Research CouncilAustralian Health Practitioner Regulation AgencyNational Health and Medical Research CouncilWishlist
KeywordsBayley Scales of Infant DevelopmentMedicineToddlerGestational agePediatricsConfidence intervalProspective cohort studyArea under the curveCognitionInternal medicinePsychologyPregnancyDevelopmental psychologyPsychomotor learning

Abstract

fetched live from OpenAlex

AIM: This study aimed to identify early clinical biomarkers from birth to 16 weeks corrected age to predict typical outcome and developmental delay in infants born very preterm or with very low birthweight. METHOD: A prospective cohort of infants on the Sunshine Coast, Australia, was assessed using the Premie-Neuro Examination, the General Movement Assessment (GMA), the Alberta Infant Motor Scale, and the Infant Sensory Profile 2. At 24 months corrected age, delay was identified using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) and Neurosensory Motor Developmental Assessment (NSMDA). RESULTS: One hundred and four infants were recruited; 79 completed outcome assessments (43 females, 36 males; mean gestational age 30 weeks [SD 1 week 6 days], mean birthweight 1346 g [SD 323]). The incidence of developmental delay (motor or cognitive) was 6.3%. Suboptimal quality of fidgety general movements (temporal organization) at 16 weeks corrected age demonstrated the best predictive accuracy (Bayley-III motor: sensitivity 100% [95% confidence interval {CI} 3-100], specificity 75% [95% CI 63-84], area under the curve [AUC] 0.87); Bayley-III cognitive: sensitivity 100% [95% CI 3-100], specificity 75% [95% CI 64-84], AUC 0.88); NSMDA motor: sensitivity 100% [95% CI 40-100], specificity 81% [95% CI 70-90], AUC 0.91 [95% CI 0.86-0.95]). GMA trajectories that combined abnormal writhing general movements at 4 to 5 weeks corrected age with suboptimal quality of fidgety movement at 16 weeks corrected age were strongly predictive of developmental delay, superior to all other clinical tools, and perinatal and demographic variables investigated (p = 0.01, Akaike information criterion method 18.79 [score corrected for small sample size], accounting for 93% of the cumulative weight). INTERPRETATION: Only the GMA had sufficient predictive validity to act as a biomarker for both conditions: typical outcome and developmental delay (motor or cognitive). GMA trajectories that assessed both writhing general movements at 4 to 5 weeks corrected age and quality of fidgety movement at 16 weeks corrected age predicted adverse neurodevelopmental outcome, accurately differentiating between infants with typical outcomes and those at increased risk for motor or cognitive delay.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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 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

Citations23
Published2022
Admission routes1
Has abstractyes

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