Malnutrition is common in Ugandan children with cerebral palsy, particularly those over the age of five and those who had neonatal complications
Bibliographic record
Abstract
AIM: Poor growth and malnutrition are frequently reported in children with cerebral palsy in developed countries, but there is limited information from developing countries. We investigated the nutritional status of Ugandan children with cerebral palsy and described the factors associated with poor nutrition. METHODS: We examined 135 children from two to 12 years with cerebral palsy, who attended Uganda's national referral hospital. A child was considered underweight, wasted, stunted or thin if the standard deviation scores for their weight for age, weight for height, height for age and body mass index for age were ≤-2.0 using World Health Organization growth standards. Multivariable logistic regression identified the factors associated with nutritional indicators. RESULTS: Over half (52%) of the children were malnourished, with underweight (42%) being the most common category, followed by stunting (38%), thinness (21%) and wasting (18%). Factors that were independently associated with being malnourished were as follows: presence of cognitive impairment, with an adjusted odds ratio (aOR) of 4.5, being 5 years or older (aOR = 3.4) and feeding difficulties in the perinatal period (aOR = 3.2). CONCLUSION: Malnutrition was common in Ugandan children with cerebral palsy and more likely if they were 5 years or more or had experienced neonatal complications.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".