Factors Associated with Low Birth Weight in Indigenous Populations: a systematic review of the world literature
Bibliographic record
Abstract
Abstract Objectives: we aimed to identify etiological factors for low birth weight (LBW), prematurity and intrauterine growth restriction (IUGR) in the Indigenous Population. Methods: for this systematic review, publications were searched in Medline/PubMed, Scopus, Web of Science, and Lilacs until April 2018. The description in this review was based on the PRISMA guideline (Study protocol CRD42016051145, registered in the Centre for Reviews and Dissemination at University of York). We included original studies that reported any risk factor for one of the outcomes in the Indigenous Population. Two of the authors searched independently for papers and the disagreements were solved by a third reviewer Results: twenty-four studies were identified, most of them were from the USA, Canada and Australia. The factors associated were similar to the ones observed in the non-indigenous including unfavorable obstetric conditions, maternal malnutrition, smoking, and maternal age at the extremes of childbearing age, besides environmental factors, geographic location, and access to health care in indigenous communities. Conclusions: etiologic factors for LBW in Indigenous Population have been receiving little attention, especially in Latin America. The three outcomes showed common causes related to poverty and limited access to healthcare. New studies should ensure explicit criteria for ethnicity, quality on the information about gestational age, and the investigation on contextual and culture-specific variables.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".