Risk factors for mortality among children hospitalized because of acute respiratory infections in Bangui, Central African Republic
Bibliographic record
Abstract
BACKGROUND: Acute respiratory infections are the most common cause of death in children in developing countries. Little information is available on risk factors for mortality among African children presenting with symptoms compatible with acute respiratory infections. OBJECTIVE: To identify risk factors for death among children hospitalized for respiratory complaints who satisfy the WHO clinical definition for pneumonia or severe pneumonia. METHODS: Children <5 years of age who presented with cough and/or difficult breathing and were hospitalized in Bangui during a 1-year period were investigated for risk factors for mortality. The study population consisted of 395 children who satisfied the WHO clinical definition for pneumonia/severe pneumonia. The associations between death and demographic, nutritional, socioeconomic, laboratory and clinical variables were examined. RESULTS: Of the 49 (12.4%) children who died, all but one had had indrawing of the chest which, in univariate analysis, was the risk factor most strongly associated with death [odds ratio, 22.99; 95% confidence interval (CI), 3.81 to 935.2]. In a multivariate model the independent risk factors for death were indrawing of the chest [adjusted odds ratio (AOR) 8.35, CI 1.04 to 66.82], hepatomegaly (AOR 6.72, CI 2.35 to 19.21), age between 2 and 11 months (AOR 6.37, CI 2.18 to 18.59), grunting (AOR 4.53, CI 1.96 to 10.45), a moderate/severe alteration of general status (AOR 3.23, CI 1.17 to 8.94) and acute malnutrition (AOR 2.74, CI 0.96 to 7.78). CONCLUSIONS: These findings could be used in flow charts for the management of children with respiratory complaints to identify children at increased risk of death who need to receive aggressive therapy.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".