Diarrhoea in slum children: observation from a large diarrhoeal disease hospital in <scp>D</scp>haka, <scp>B</scp>angladesh
Bibliographic record
Abstract
OBJECTIVES: To determine and compare socio-demographic, nutritional and clinical characteristics of children under five with diarrhoea living in slums with those of children who do not live in slums of Dhaka, Bangladesh. METHODS: From 1993 to 2012, a total of 28 948 under fives children with diarrhoea attended the Dhaka Hospital of icddr,b. Data were extracted from the hospital-based Diarrhoea Disease Surveillance System, which comprised 17 548 under fives children from slum and non-slum areas of the city. RESULTS: Maternal illiteracy [aOR = 1.57; 95% confidence interval (1.36, 1.81), P-value <0.001], paternal illiteracy [1.37 (1.21, 1.56) <0.001], mother's employment [1.59 (1.37, 1.85) <0.001], consumption of untreated water [2.73 (2.26, 3.30) <0.001], use of non-sanitary toilets [3.48 (3.09, 3.93) <0.001], 1st wealth quintile background [3.32 (2.88, 3.84) <0.001], presence of fever [1.14 (1.00, 1.29) 0.047], some or severe dehydration [1.21 (1.06, 1.40) 0.007], stunting [1.14 (1.01, 1.29) 0.030] and infection with Vibrio cholerae [1.21 (1.01, 1.45) 0.039] were significantly associated with slum-dwelling children after controlling for co-variates. Measles immunisation [0.52 (0.47, 0.59) P < 0.001] and vitamin A supplementation rates [0.36 (0.31, 0.41) P < 0.001] amongst children 12-59 months were lower for slum dwellers than other children in univarate analysis only. CONCLUSIONS: Slum-dwelling children are more malnourished, have lower immunisation rates (measles vaccination and vitamin A supplementation) and higher rates of measles, are more susceptible to diarrhoeal illness due to V. cholerae and suffer from severe dehydration more often than children from non-slum areas. Improved health and nutrition strategies should give priority to children living in urban slums.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".