Contribution of Dehydration and Malnutrition to the Mortality of Children 0-59 Month of Age in a Senegalese Pediatric Hospital
Bibliographic record
Abstract
In-hospital mortality is an indicator of the quality of care. We analyzed the mortality of under five years children of Pediatric ward of Aristide Le Dantec teaching hospital to update our data, after an previous study conducted ten years earlier. Methods: This was a retrospective study involving children 0-59 months of age, hospitalized from January 1, 2012 to December 31, 2012. For each child, nutritional status was assessed according to 2006 World Health Organization growth standards; clinical and biological data were recorded. The outcome of the disease was specified. Bivariate and multivariable were used to identify risk factors for death. Results: 393 children were included. Overall mortality rate was 10% (39/393). Factors associated with death were severe wasting [OR = 8.27, 95% CI [3.79-18], male gender (OR = 2.98, 95% CI [1.25-7.1]), dehydration (OR = 5.4, 95% CI [2.54-13.43]) in the model using the weight-for- height z score, male gender (OR = 2.5, 95% CI [1.11-5.63]), dehydration (OR = 8.43, 95% CI [3.83-18.5]) in using the height- for- age z score, male gender (OR = 2.7, 95% CI [1.19-6.24]), dehydration (OR = 7.5, 95% CI [3.39-16.76]), severe underweight (OR = 2.4, 95% CI [1.11-5.63]), in the model using the weight-for- age z score, and male gender (OR = 2.5, 95% CI [1.11-5.63]), dehydration (OR = 8.43, 95% CI [3.83-18.5]) in that using MUAC. Dehydration and malnutrition are two independent factors of mortality. Our management protocols of dehydration and malnutrition have to be updated. Screening malnutrition has to be done systematically for each child by anthropometric measurements using WHO growth standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".