Prevalence of Para-Pneumonic Effusion and the Associated Factors among Children: A 3-Year Experience in a Single Tertiary Hospital
Bibliographic record
Abstract
Background: Previous studies have highlighted the significant morbidity associated with para-pneumonic effusions in pediatric populations. However, comprehensive data on the prevalence and associated factors in children remain limited, particularly in tertiary care settings. Methods: A retrospective cohort study was conducted over a three-year period at a single tertiary hospital. Medical records of pediatric patients diagnosed with pneumonia were reviewed to identify cases of para-pneumonic effusion. The study analyzed demographic data, clinical presentations, laboratory findings, and management approaches to determine factors associated with the development of para-pneumonic effusions. Results: 150 patients were identified with para-pneumonic effusions. The majority of patients with effusions were male (60%) and under five years of age (70%). Factors significantly associated with the development of effusions included the presence of comorbidities (p<0.01), higher CRP levels (p<0.05), and hospital-acquired pneumonia (p<0.001). The length of hospital stay was notably longer in patients with effusions than those without (p<0.001). Conclusion: The study found a considerable prevalence of para-pneumonic effusions among children with pneumonia in a tertiary hospital setting. Notably, associated factors were underlying comorbidities, elevated inflammatory markers, and hospital-acquired infections. These findings underscore the need for heightened surveillance and tailored management strategies in high-risk pediatric populations to mitigate the impact of effusions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".