Gestational Weight Gain and Undernutrition in Pregnant Women in India: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Maternal undernutrition and inadequate gestational weight gain (GWG) remain significant public health concerns in India, contributing to adverse maternal and neonatal outcomes. Despite the critical importance of appropriate GWG for healthy pregnancy outcomes, comprehensive synthesized evidence specific to Indian populations is limited. Objectives: This systematic review and meta-analysis protocol aims to: (1) estimate the pooled mean gestational weight gain among pregnant women in India; (2) determine the prevalence of maternal undernutrition (BMI <18.5 kg/m²); and (3) quantify proportions of pregnant women achieving inadequate, adequate, or excessive GWG within BMI categories based on Institute of Medicine (IOM) Methods: We will systematically search PubMed, Embase, Scopus, Web of Science, and IndMED for observational studies published from 2000 onwards. Two independent reviewers will screen titles, abstracts, and full texts, extract data using standardized forms, and assess study quality using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist and Newcastle-Ottawa Scale (NOS). Random-effects meta-analyses will be conducted to pool mean GWG, prevalence of undernutrition, and GWG adequacy proportions. Heterogeneity will be assessed using I² statistics, and subgroup analyses will explore variations by geographic region, urban/rural setting, study quality, and publication year. Publication bias will be evaluated using funnel plots and Egger’s test. The certainty of evidence will be assessed using the GRADE framework. Discussion: This review will provide robust, pooled estimates of GWG patterns and maternal undernutrition prevalence in India, informing evidence-based maternal nutrition interventions and policy development. The findings will highlight gaps in current knowledge and identify priority areas for future research. Systematic Review Registration: OSF. Keywords: gestational weight gain, maternal undernutrition, body mass index, pregnancy, India, systematic review, meta-analysis, protocol
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".