Nutritional evaluation and growth of infants in a Rwandan neonatal follow‐up clinic
Bibliographic record
Abstract
Children born preterm, low birth weight (LBW) or with other perinatal risk factors are at high-risk of malnutrition. Regular growth monitoring and early intervention are essential to promote optimal feeding and growth; however, monitoring growth in preterm infants can be complex. This study evaluated growth monitoring of infants under 6 months enrolled in Paediatric Development Clinics (PDCs) in rural Rwanda. We reviewed electronic medical records (EMR) of infants enrolled in PDCs before age 2 months with their first visit between January 2015 and December 2016 and followed them until age 6 months. Nurse classification of anthropometric measures and nutritional status were extracted from the EMR. Interval growth and length-for-age, weight-for-length, and weight-for-age z-scores were calculated using World Health Organization anthropometry software as a 'gold standard' comparison to nurse classifications. Two hundred and ninety-four patients enrolled and had 2,033 visits during the study period. Referral reasons included prematurity/LBW (73.8%) and hypoxic ischemic encephalopathy (28.2%). Nurses assessed interval growth at 58.7% of visits, length-for-age at 66.4%, weight-for-length at 65.6% and weight-for-age at 66.4%. Nurses and gold standard assessment agreed on interval growth at 53.3% of visits and length-for-age at 63.7%, weight-for-length at 78.2% and weight-for-age at 66.3%. At 6 months, 46.5% were stunted, 19.9% were wasted and 44.2% were underweight. There were significant challenges to optimizing growth and growth monitoring among high-risk infants served by PDCs, including incomplete and inaccurate assessments. Developing tools for clinician decision support in assessing growth and providing specialized nutritional counselling are essential to supporting optimal outcomes in this population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".