Pattern and Determinants of Gestational Weight Gain an Important Predictor of Infant Birth Weight in a Developing Country
Bibliographic record
Abstract
The study aimed to determine correlates of gestational weight gain and infant birth weight of pregnant women attending antenatal clinics in public primary health care facilities in lfe Central and East Local Government Areas of Osun State, Nigeria. Over 1000 women were recruited during booking and antenatal clinic and followed up till delivery. Chi square was used in the bivariate analysis of association between gestational weight gain, pre pregnancy BMI and demographic characteristics. The correlates of gestational weight gain and infant birth weight were determined by linear regression analysis. Eight percent are underweight, 10.3% are overweight or obese, 78% had a weight gain less than 7kg and 0.5% had a weight gain above 11.5kg. Ninety seven percent gained less than recommended weight, only 3% of the women gained the recommended weight for their pre pregnant BMI mostly the obese women. Twenty eight percent of the women had infant weight within normal (2.5kg and above) .The infant weight increases with the gestational age, maternal age and parity but decreases with gestational weight gain though not significant. Maternal age and parity were significant predictors of gestational weight gain and. pre pregnancy BMI was a significant predictor of infant birth weight. The gestational weight gain and infant weight reduces as the pre pregnant BMI increases. Most of the women had low birth weight babies. There is a need to educate mothers on good weight before conception in order to improve birth outcome in view of other factors not looked into in the present study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".