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Record W2288718379 · doi:10.1136/bmj.i555

Gestational weight gain standards based on women enrolled in the Fetal Growth Longitudinal Study of the INTERGROWTH-21<sup>st</sup>Project: a prospective longitudinal cohort study

2016· article· en· W2288718379 on OpenAlexaff
Leila Cheikh Ismail, Deborah C. Bishop, Ruyan Pang, Eric O. Ohuma, Gilberto Kac, Barbara Abrams, Kathleen M. Rasmussen, Fernando C. Barros, Jane E. Hirst, Ann Lambert, Aris T. Papageorghiou, William Stones, Yasmin A. Jaffer, Douglas G. Altman, J. Alison Noble, Maria Rosa Giolito, Michael G. Gravett, Manorama Purwar, Stephen Kennedy, Zulfiqar A Bhutta, José R. Villar

Bibliographic record

VenueBMJ · 2016
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsSickKids FoundationCentre for Global Health ResearchHospital for Sick Children
Fundersnot available
KeywordsMedicineGestationGestational agePregnancyBody mass indexWeight gainObstetricsProspective cohort studyBirth weightLongitudinal studyCohort studyFetusBody weightInternal medicineBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe patterns in maternal gestational weight gain (GWG) in healthy pregnancies with good maternal and perinatal outcomes. DESIGN: Prospective longitudinal observational study. SETTING: Eight geographically diverse urban regions in Brazil, China, India, Italy, Kenya, Oman, United Kingdom, and United States, April 2009 to March 2014. PARTICIPANTS: Healthy, well nourished, and educated women enrolled in the Fetal Growth Longitudinal Study component of the INTERGROWTH-21(st) Project, who had a body mass index (BMI) of 18.50-24.99 in the first trimester of pregnancy. MAIN OUTCOME MEASURES: Maternal weight measured with standardised methods and identical equipment every five weeks (plus/minus one week) from the first antenatal visit (<14 weeks' gestation) to delivery. After confirmation that data from the study sites could be pooled, a multilevel, linear regression analysis accounting for repeated measures, adjusted for gestational age, was applied to produce the GWG values. RESULTS: 13,108 pregnant women at <14 weeks' gestation were screened, and 4607 met the eligibility criteria, provided consent, and were enrolled. The variance within sites (59.6%) was six times higher than the variance between sites (9.6%). The mean GWGs were 1.64 kg, 2.86 kg, 2.86 kg, 2.59 kg, and 2.56 kg for the gestational age windows 14-18(+6) weeks, 19-23(+6) weeks, 24-28(+6) weeks, 29-33(+6) weeks, and 34-40(+0) weeks, respectively. Total mean weight gain at 40 weeks' gestation was 13.7 (SD 4.5) kg for 3097 eligible women with a normal BMI in the first trimester. Of all the weight measurements, 71.7% (10,639/14,846) and 94.9% (14,085/14,846) fell within the expected 1 SD and 2 SD thresholds, respectively. Data were used to determine fitted 3rd, 10th, 25th, 50th, 75th, 90th, and 97th smoothed GWG centiles by exact week of gestation, with equations for the mean and standard deviation to calculate any desired centiles according to gestational age in exact weeks. CONCLUSIONS: Weight gain in pregnancy is similar across the eight populations studied. Therefore, the standards generated in this study of healthy, well nourished women may be used to guide recommendations on optimal gestational weight gain worldwide.

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)0.004
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.021
GPT teacher head0.326
Teacher spread0.305 · 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

Citations173
Published2016
Admission routes1
Has abstractyes

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