The Effect of Moringa-Based Supplementation on Fetal Birth Weight in Jeneponto Regency
Bibliographic record
Abstract
Background: Low birth weight (LBW) is one of the main causes of morbidity and mortality besides preterm birth. Proper interventions during pregnancy can prevent an adverse pregnancy outcome. This study aims to see the effect of Moringa leaf capsule on birth weight.Methods: This study was double blind randomized controlled trial (DB-RCT) which consisted of three groups namely, Moringa powder (PG), Moringa extract (EG), and iron-folate (IG) groups. The intervention was given for 12 weeks. The samples were 453 pregnant women in six sub-districts in Jeneponto Regency. Data on birth weight and placental weight were measured by trained midwives. The weight of the placenta was measured to determine the placental ratio to birth weight. In addition, some socio-economic variables such as age, gestational age, eating frequency, smoking, and Hb levels were measured in this study. Logistic and linear regression were conducted in this study.Result: The Moringa leaf supplementation groups (PG and EG) delivered child with better weight than iron supplementation (3240.03±453.82, 3161.91±527.70, 3100.89±412.15, respectively). The placenta to birth weight ratio (PBWR) showed that IG group became lowest, merely 16.19%. The most influenced factor to LBW is the unhappiness of the women with her pregnancy (OR = 26.3, 95% CI = 1.227 - 566.474, p = 0.037).Conclusion: Moringa powder supplementation can be used as an alternative in improving new born baby weight. Pregnant women need to be happy and avoid stress to prevent LBW.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".