Evolution of intrapleural bleeding in pediatric empyema management : a 10-year single-center study assessing associated risk factors
Bibliographic record
Abstract
BACKGROUND: Pediatric empyema requires hospitalization, broad-spectrum antibiotics, and thoracic drainage with intrapleural fibrinolytics (IPF). Intrapleural bleeding is the main cause of early IPF cessation. While adult studies identified bleeding risk factors, pediatric data are limited, and adult findings may not apply due to different comorbidities. This study describes intrapleural bleeding rates in pediatric empyema over 10 years and explores associated risk factors. METHODS: We retrospectively analyzed clinical, radiological, and microbiological data of patients <18 years admitted to CHU Sainte-Justine (2014–2024) for empyema, identified via ICD-10 codes and clinical lists. Intrapleural bleeding was documented from treating physicians’ notes. Trends and potential risk factors were assessed. RESULTS: Among 260 patients, 238 (91.5%) had chest drainage (median duration: 6 days). IPF was used in 195 (82%) cases, with a median of 3 doses. Bleeding occurred in 23 (9.6%) cases, but no transfusions were required. Bleeding rates increased over time (p<0.01), with no identified clinical, radiological, or biological risk factors. However, an association was found with rhinovirus/enterovirus co-infection (p<0.01). CONCLUSIONS: Intrapleural bleeding rates increased over 10 years, with no clear risk factors except for a possible association with rhinovirus/enterovirus. This descriptive study highlights the need for further research to confirm this finding and improve risk assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".