Grey matter brain injuries are common in Ugandan children with cerebral palsy suggesting a perinatal aetiology in full‐term infants
Bibliographic record
Abstract
AIM: There is limited literature on brain imaging studies of children with cerebral palsy (CP) in low and middle income countries. We investigated neuroimaging patterns of children with CP attending a tertiary referral centre in Uganda to determine how they differed from studies reported from high income countries and their relationship with prenatal and postnatal factors. METHODS: Precontrast and postcontrast computed tomography (CT) scans of 78 CP children aged 2-12 years were conducted using a Philips MX 16-slice CT scanner. Two radiologists, blinded to the patient's clinical status, independently reviewed the scans. RESULTS: Abnormal CT scans were detected in 69% of the children sampled, with very few having primary white matter injuries (4%). Primary grey matter injuries (PGMI) (44%) and normal scans (31%) were most frequent. Children with a history of hospital admission following birth were three times more likely to have PGMI (odds ratio [OR] 2.8; 95% CI 1.1-7.1), suggesting a perinatal period with medical complications. CONCLUSION: Brain imaging patterns in this group of CP children differed markedly from imaging studies reported from high income countries, suggesting a perinatal aetiology in full-term infants and reduced survival in preterm infants.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".