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Record W4416392848 · doi:10.3390/life15111773

Association Between Birth Outcomes and Gestational Weight Gain Among Forcibly Displaced Rohingya and Nearby Host Community, in Cox’s Bazar, Bangladesh

2025· article· en· W4416392848 on OpenAlexfundno aff
Md Shakil Ahamed, Elisa Ugarte, Mahbub Elahi, Eamam Hossain, M Sajjadur Rahman, Kazi Istiaque Sanin, Abir Dutta, Goutam Kumar Dutta, Alice J. Wuermli, Fahmida Tofail

Bibliographic record

VenueLife · 2025
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
FundersInternational Centre for Diarrhoeal Disease Research, BangladeshYork UniversityNew York University
KeywordsUnderweightWeight gainBirth weightConfoundingGestational agePregnancyGestationCohort study

Abstract

fetched live from OpenAlex

Gestational weight gain (GWG) is a critical determinant of maternal and neonatal health, yet its patterns and consequences in displaced populations remain understudied. This study examined the association between GWG and birth outcomes among Forcibly Displaced Rohingya (FDR) women in Cox’s Bazar, Bangladesh. We conducted a longitudinal cohort study from October 2022 to October 2024, enrolling 2888 pregnant women at different stages of pregnancy. Among them, 301 were recruited in the first trimester and followed through the third trimester, with 231 neonatal outcomes recorded within 72 hours of delivery. Overall, 66.8% of women experienced inadequate GWG. Despite the high prevalence of inadequate GWG, mean birth weight (2.79 kg) and mean gestational age at delivery (38.6 weeks) were within favorable ranges. Inadequate GWG was more common in mothers aged 30–39 years (p = 0.061) but significantly less common in underweight mothers (p = 0.012). GWG was positively associated with neonatal birth weight, length, and weight–length ratio (WLR) Z score, but not with gestational age. After adjusting for confounding factors, inadequate GWG showed a significant independent association with lower birth length (p = 0.016). These findings highlight the need for targeted interventions in displaced populations.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.295
Teacher spread0.280 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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