The Effects of Antenatal Interventions on Gestational Weight Gain in Low- and Middle-Income Countries: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Gestational weight gain (GWG) is a crucial determinant of maternal and child outcomes yet remains an underused target for antenatal interventions in low- and middle-income countries (LMICs). OBJECTIVE: This systematic review aims to identify and summarize educational, behavioral, nutritional, and medical interventions on GWG from randomized controlled trials conducted in LMICs. METHODS: Randomized controlled trials that documented the effects of antenatal interventions on GWG in LMICs will be included. The interventions of interest will be educational, behavioral, nutritional, or medical. A systematic literature search will be conducted using PubMed, Embase, Web of Science, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and the Cochrane Library from the inception of each database through October 2022 (with an updated search in January 2024). A total of 2 team members will independently perform the screening of studies and data extraction. A narrative synthesis of all the included studies will be provided. The risk of bias will be assessed using the Cochrane Risk of Bias tool. The certainty of the evidence for each homogeneous group of interventions will be assessed using the GRADE (Grading of Recommendation, Assessment, Development, and Evaluation) approach. A narrative synthesis of the included studies will be conducted to summarize mean differences (with 95% CIs) for continuous outcomes and risk ratios, rate ratios, hazard ratios, or odds ratios (with 95% CIs) for dichotomous or categorical outcomes. Available information on the costs of interventions will also be summarized to facilitate the adoption and scale-up of effective GWG interventions. RESULTS: The development of the research questions, search strategy, and search protocol was started on September 20, 2022. The database searches and the importation of the identified records into Covidence were performed on October 7, 2022. As of September 2023, the title and abstract screening was ongoing. The target completion time of this systematic review is April 2024. CONCLUSIONS: Without effective interventions to manage GWG, the potential to improve maternal and child health through optimal GWG remains unrealized in LMICs. This systematic review will inform the design and implementation of antenatal interventions to prevent inadequate and excessive GWG in resource-limited settings. TRIAL REGISTRATION: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022366354; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=366354. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48234.
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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 | 0.073 | 0.090 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.023 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.064 | 0.008 |
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".