Prevalence, Clinical Features, and Outcome of <i>Pseudomonas</i> Bacteremia in Under-Five Diarrheal Children in Bangladesh
Bibliographic record
Abstract
We sought to evaluate the prevalence, associated factors, and outcome of under-five diarrheal children with either sex having Pseudomonas bacteremia. A retrospective chart review of under-five diarrheal children admitted to the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), from January 2011 to December 2011 was performed using an online hospital management system. Children with Pseudomonas bacteremia constituted the cases (n = 31), and the controls (n = 124), without Pseudomonas bacteremia, were randomly selected. The prevalence of Pseudomonas bacteremia was 1% (31/5,179). The Pseudomonas was multidrug resistant but was 84% sensitive to ceftazidime and 100% to imipenem. The case-fatality rate was significantly higher among the cases than the controls (26% versus 5%; P = 0.003). In logistic regression analysis, after adjusting for potential confounders such as severe wasting, severe underweight, severe pneumonia, and young age (11.71 (4.0, 18.0) months), the cases more often presented with absent peripheral pulses in absence of dehydration (95% CI = 2.31-24.45) on admission. This finding underscores the importance of early identification of this simple clinical sign to ensure prompt management including fluid resuscitation and broad spectrum antibiotics to help reduce morbidity and mortality in such children, especially in resource-poor settings.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".