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Record W4388499127 · doi:10.1016/j.ajogmf.2023.101220

Predictive ability of fetal growth charts in identifying kindergarten-age developmental challenges: a cohort study

2023· article· en· W4388499127 on OpenAlexafffund
Ariadna Fernandez, Jessica Liauw, Chantal Mayer, Arianne Albert, Jennifer A. Hutcheon

Bibliographic record

VenueAmerican Journal of Obstetrics & Gynecology MFM · 2023
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsWomen's Health Research InstituteUniversity of British ColumbiaB.C. Women's Hospital & Health Centre
FundersBC Children's Hospital
KeywordsPercentileMedicineCohortPopulationGeneration RCohort studyObstetricsConfidence intervalGrowth chartPediatricsDemographyStatisticsEnvironmental healthMathematicsInternal medicine

Abstract

fetched live from OpenAlex

Background The Society for Maternal-Fetal Medicine recommends defining fetal growth restriction as an estimated fetal weight or abdominal circumference below the 10th percentile of a population-based reference. However, as multiple references are available, an understanding of their ability to identify infants at increased risk due to fetal growth restriction is critical. Previous studies have focus on the ability of different population references to identify short-term outcomes, but fetal growth restriction also has longer-term consequences for child development. Objectives To estimate the association between estimated fetal weight (EFW) percentiles on the INTERGROWTH-21 st and WHO fetal growth charts and kindergarten-age childhood development, and establish the charts' discriminatory ability in predicting kindergarten-age developmental challenges. Study Design We conducted a retrospective cohort study linking obstetrical ultrasound scans conducted at BC Women's Hospital, Vancouver, Canada, with population-based standardized kindergarten test results. The cohort was limited to non-anomalous, singleton fetuses scanned ≥ 28 weeks' gestation, 2000-2011, with follow-up to 2017. We classified EFWs into percentiles using the INTERGROWTH-21st and WHO charts. We used generalized additive modelling to link EFW percentile with routine province-wide kindergarten readiness test results. We calculated the area under the receiver-operating characteristic curve (AUC), as well as other measures of diagnostic accuracy with 95% confidence intervals (CI) at select percentile cut-points of the charts. We repeated analyses using the Hadlock chart to help contextualize findings. The main outcome measure was the total Early Development Instrument (EDI) score (/50). Secondary outcomes were EDI sub-domain scores for language and cognitive development, and for communication skills and general knowledge; designation of ‘developmentally vulnerable' or ‘special needs'. Results Among 3418 eligible fetuses, those with lower EFW percentiles had systematically lower EDI scores and increased risks of developmental vulnerability. However, the clinical significance of differences was modest in magnitude: e.g., total EDI score -2.8 [95% CI: -5.1, -0.5] in children with an EFW 3-9 th percentile of INTERGROWTH chart (vs. reference of 31-90 th ). The charts' predictive abilities for adverse child development were limited (e.g., AUC<0.53 for all 3 charts). Conclusions Lower EFW percentiles on the INTERGROWTH-21 st and WHO charts indicate increased risks of adverse kindergarten-age child development at the population level, but are not accurate individual-level predictors of adverse child development.

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)0.010
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.039
GPT teacher head0.298
Teacher spread0.259 · 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

Citations2
Published2023
Admission routes2
Has abstractno

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