Motor and cognitive outcomes of neonates with low birth weight in Brazil: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Data on the outcomes of preterm newborns in South American countries are scarce. Given the great effect of low birth weight (LBW) and/or prematurity on children's neurodevelopment, it is extremely necessary to conduct studies on these phenomena in greater depth in more heterogeneous populations such as those ones from countries with limited resources. Methods We conducted a comprehensive literature search on databases including PubMed, the Cochrane Library, and Web of Science for articles published in Portuguese and English up to March 2021 involving children born and evaluated in Brazil. The analysis of the risk of bias was adapted from the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and used to evaluate the methodology of the included studies. Results From the eligible trials, 25 articles were selected for qualitative synthesis, and 5 of those, for quantitative synthesis (meta-analysis). The meta-analyses showed that children born with LBW presented lower scores on motor development when compared with controls (standardized mean difference: −1.15; 95% confidence interval [95%CI]: −1.56–−0.73]; I2: 80%) and also scored lower in terms of cognitive development (standardized mean difference: −0.71; 95% CI: −0.99–−0.44; I2: 67%). Conclusion The results of the present study reinforce that impaired motor and cognitive functions can be a significant long-term outcome of LBW. The lower the gestational age at delivery, the higher the risk of impairment in those domains. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database under number CRD42019112403.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".