Sex as a determinant of disease severity and clinical outcome in febrile children under five presenting to a regional referral hospital in Uganda
Bibliographic record
Abstract
Sex and gender are well-established determinants of health in adult and adolescent populations in low resource settings. There are limited data on sex as a determinant of host response to disease and clinical outcome in febrile children in sub-Saharan Africa, where the risk of infection-related mortality is greatest. We examined sex differences and gender biases in health-seeking behavior, clinical care, biological response to infection, or outcome in a prospective observational cohort of febrile children under 5 years of age presenting to a regional referral hospital in Jinja, Uganda. Main outcomes (stratified by sex) were disease severity at presentation measured by clinical and biological parameters, clinical management (e.g., time to see a physician, treatment by diagnosis), and disease outcome (e.g., mortality). Clinical measures of disease severity included Lambaréné Organ Dysfunction Score (LODS), Signs of Inflammation in Children that Kill (SICK), and the Pediatric Early Death Index for Africa (PEDIA). Biological measures of disease severity were assessed using circulating markers of immune and endothelial activation associated with severe and fatal infections. Differences in outcome by sex were analyzed using bivariate analyses with Bonferroni correction for multiple comparisons. In this cohort of febrile patients admitted to hospital (n = 2049), malaria infection was common (59.2%). 15.9% of children presented with severe disease (LODS score ≥ 2). 97 children (4.7%) died, and most deaths (n = 83) occurred within 48 hours of hospital admission. Clinical measures of disease severity at presentation, clinical management, and outcome (e.g., mortality) did not differ by sex in children under five years of age. Host response to infection, as determined by endothelial and inflammatory mediators (e.g., sTREM1, Ang-2) quantified at hospital presentation, did not differ by sex. In this cohort of children under the age of five, sex was not a principal determinant of disease severity at hospital presentation, clinical management, disease outcome, or biological response to infection (p-values not significant for all comparisons, after Bonferroni correction). The results suggest that health seeking behavior by caregivers and clinical care in the hospital setting did not reflect a gender bias in this cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".