A 4 year prospective study to determine risk factors for severe community acquired pneumonia in children in southern China
Bibliographic record
Abstract
BACKGROUND: Pneumonia is the major cause of death under 5 years. With high CAP numbers in China and growing access to PICUs, factors associated with severe CAP need to be determined to optimize care. OBJECTIVE: To prospectively determine PICU CAP admission features and outcomes. METHODS: A 4 year prospective study of CAP aged 1 month to <14 years admitted to PICU, Children's Hospital Affiliated to Soochow University, China. All were managed in a standard manner. Clinical, laboratory, and imaging findings were collected systematically. All received antibiotics. RESULTS: Eight hundred ten (7%) of 10,836 CAP hospital admissions needed PICU. Seven hundred seven (87%) were enrolled. PICU CAP children were young (76% ≤ 12 months) and 33% had co-morbid conditions; 21% congenital heart disease.21% required mechanical ventilation. The average length of PICU stay was 5 days (range, 3-27). The case fatality rate was 5.8%. Viruses were detected in 38%, RSV 24%; bacteria in 23%, Streptococcus pneumoniae 7%, Haemophilus influenza b 4%, Mycoplasma 11%. On single factor analysis, PICU admission respiratory rate >70/min, grunting/groaning, head nodding, cyanosis, and anemia were associated with respiratory failure and with fatality. On multivariate analysis only presence of congenital heart disease, Trisomy 21 and immunodeficiency correlated with fatality; not microbe nor PICU findings. CONCLUSIONS: Young age and underlying congenital heart disease were associated factors for PICU support in CAP in China. Early referral if altered sensorium, high respiratory rate, head nodding, grunting and anemia, and universal access to conjugated vaccines may decrease morbidity and mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".