Refined Lab-score, a Risk Score Predicting Serious Bacterial Infection in Febrile Children Less Than 3 Years of Age
Bibliographic record
Abstract
BACKGROUND: The identification of serious bacterial infection (SBI) in children with fever without source remains a challenge. A risk score called Lab-score, based on C-reactive protein, procalcitonin and urinary dipstick results was derived to predict SBI. However, all biomarkers were initially dichotomized, leading to weak statistical reliability and lack of transportability across diverse settings. We aimed to refine and validate this risk-score algorithm. METHODS: The Lab-score was refined using a secondary analysis of a multicenter cohort study of children with fever without source via multilevel regression modeling. The external validation was conducted on data from a Canadian cohort study. RESULTS: Eight hundred seventy-seven children (24% SBI) were included for the derivation study, and 347 (16% SBI) for validation. Only C-reactive protein, procalcitonin, age and urinary dipstick remained independently associated with SBI. The model achieved an area under the receiver operating characteristic (ROC) curve of 0.94 (95% confidence interval [CI]: 0.93-0.96), which was significantly higher than any other isolated biomarker (P < 0.0001), and the original Lab-score (P < 0.0001). According to a decision curve analysis, the model yielded a better strategy than those based on independently considered biomarkers, or on the original Lab-score. The threshold analysis led to a cutoff that yielded 96% (95% CI: 92-98) sensitivity and 73% (95% CI: 70-77) specificity. The external validation found similar predictive abilities: 0.96 area under the ROC curve (95% CI: 0.93-0.99), 95% sensitivity (95% CI: 85-99) and 87% specificity (95% CI: 83-91). CONCLUSION: The refined Lab-score demonstrated higher prediction ability for SBI than the original Lab-score, with promising wider applicability across settings. These results require validation in additional populations.
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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 | 0.001 | 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 | 0.001 |
| 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".