The Effect of Gestational Weight Gain on Low Birth Weight, Case-Control Study in Northeast Amhara Regional State, Ethiopia, 2019
Bibliographic record
Abstract
Background: Intrauterine growth and development are one of the most vulnerable periods in the human life cycle that contribute to appropriate fetal development. Therefore, this study aimed to assess the effect of gestational weight on low birth weight (LBW). Methods: A case-control study was conducted from June 30, 2018, to January 1, 2019, in seven governmental hospitals in the northeast Amhara region on 451 participants(150 cases and 301 controls). Results: Inadequate gestational weight gain increases the risk of LBW by four times (AOR: 4.2, 95% CI: 2.4, 6.4). Anemic mothers were 3 times (AOR: 3.2, 95%CI: 2.5, 5.1) more likely to give birth to LBW newborns than non-anemic women. Mothers with a height of less than 150 cm were 2 times more likely to deliver low birth weight babies than their counterparts (AOR:2.1, 95% CI: 1.5,4.4). The odds of LBW delivery were 3.5 times (AOR: 3.5, 95% CI: 2.3, 5.3) higher for mothers with poor dietary diversity than for mothers with good dietary diversity. Conclusion: Inadequate gestational weight gain during pregnancy was found to be a risk factor for LBW. Additionally, anemia, short stature, and poor dietary diversity were also risk factors for LBW. Therefore, selectively targeted interventions such as improving maternal nutrition, anemia prevention, and proper maternal weight monitoring during pregnancy are needed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".