Gender differences in survival among low birthweight newborns and infants in sub-Saharan Africa: a systematic review
Bibliographic record
Abstract
Abstract In sub-Saharan Africa, low birthweight (LBW) accounts for three-quarters of under-five mortality and morbidity. However, gender differences in survival among LBW newborns and infants have not yet been systematically examined. This review examines gender differences in survival among LBW newborns and infants in the region. Ovid Medline, Embase, CINAHL, Scopus and Global Health databases were searched for qualitative, quantitative and mixed methods studies. Studies that presented information on differences in mortality or in morbidity between LBW male and female newborns or infants were eligible for inclusion. The database search yielded 4124 articles, of which 11 were eligible for inclusion. A narrative synthesis method was used to summarize the findings of the included studies. Seven studies reported more LBW male deaths, three studies reported more LBW female deaths and one study did not disaggregate the deaths by gender. Nine of the 11 studies that examined gender differences in mortality did not find significant evidence of gender differences in mortality among LBW newborns and infants. Likewise, no significant differences were found for gender differences in morbidity among this population. The review findings suggest a need for further research on this topic given the potential significance on child health and developmental goals.
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 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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".