Predictors of Disease Severity in Children Hospitalized for Pertussis During an Epidemic
Bibliographic record
Abstract
BACKGROUND: Australia recently experienced its worst pertussis epidemic since introduction of pertussis vaccine into the National Immunisation Program. This study aimed to determine factors associated with severe pertussis in hospitalized children during an epidemic using a novel pertussis severity scoring (PSS) system. METHODS: This prospective, observational, multicenter study enrolled children hospitalized with laboratory confirmed pertussis from 8 tertiary pediatric hospitals during a 12 month period (May 2009-April 2010). Variables assessed included demographics, clinical symptoms and relevant medical and immunization history. Cases were scored using objective clinical findings with cases classified as either severe (PSS > 5) or not severe (PSS ≤ 5). Logistic regression models were used to predict variables associated with severe disease. RESULTS: One hundred twenty hospitalized children 0-17 years of age were enrolled with a median PSS of 5 (interquartile range 3-7). Most (61.7%) were classified as not severe with 38.3% (46/120) severe. Most severe cases (54.3%) were <2 months of age. Presence of coinfection [odds ratio (OR): 4.82, CI: 1.66-14.00], <2 months old (OR: 4.76, CI: 1.48-15.32), fever >37.5°C (OR: 5.97, CI: 1.19-29.96) and history of prematurity (OR: 5.00, CI: 1.27-19.71) were independently associated with severe disease. A total of 70 cases in children ≥2 months of age, almost a third (n = 23) had not received pertussis vaccine. CONCLUSIONS: Most severe pertussis occurred in young, unimmunized infants, although severe disease was also observed in children >12 months of age and previously vaccinated children. Children admitted with pertussis with evidence of coinfection, history of prematurity or fever on presentation need close monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".