Growth and Malnutrition Assessment of Neonates Admitted to a Government Hospital in Nakuru, Kenya
Bibliographic record
Abstract
Background and Aims: Inadequate nutrient provision causes neonatal growth failure and malnutrition. Therefore, this study aimed to 1) quantify infant growth velocity from birth to hospital discharge, 2) determine the incidence of neonatal malnutrition at the time of discharge from a government hospital newborn unit in Nakuru, Kenya. Methods: After ethical approval, data was collected for infants (n=104) hospitalized >14 days (June 2016 - December 2018) including: birth gestational age (GA), birth and discharge weight (grams, g) with z-scores (2013 Fenton Preterm or 2006 World Health Organization 0-2 Year growth chart), hospital length of stay (LOS) days. Growth during hospitalization was calculated in g/day [(discharge weight – birth weight)/LOS] and g/kilogram(kg)/day [1000xln(birth weight/discharge weight)/LOS). Malnutrition was diagnosed by birth to discharge weight z-score change (decline): mild = 0.8-1.2 standard deviations (SD), moderate = >1.2-2.0 SD, severe = >2.0 SD. P-value <0.05 was significant. Results: 94/104 (90.4%) infants were preterm with median birth GA 32 weeks, weight 1500 g (z-score -0.33), LOS 21 days and discharge weight 1735 g (z-score -1.95). Median weight gain was 8.2 g/day or 5.2 g/kg/day with weight z-score change -1.34 SD. Linear regression predicted each hospital day decreased z-score by -0.031 (p<0.001). At discharge, 81.7% of infants met malnutrition criteria—27.1% mild, 49.4% moderate, 23.5% severe. Conclusions: Infants with LOS >14 days in a government hospital newborn unit in Nakuru, Kenya, experience growth rates below recommended velocities by the World Health Organization (23-34 grams/day from 0-4 months). Nutrition intervention is necessary to support appropriate growth.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".