Derivation and Validation of a Clinical Rule to Detect Bacteremia Versus Contaminants in Positive Pediatric Blood Cultures: A Retrospective Cohort Study
Bibliographic record
Abstract
STUDY OBJECTIVES: Fifty percent of positive blood cultures in the pediatric emergency department (ED) are contaminants. We derived and validated a clinical decision rule discriminating bacteremia from contaminants among children seen in the ED with a positive blood culture. METHODS: We used 2 cohorts of children with positive blood cultures from a Canadian pediatric ED in 2018 to 2022 (derivation) and 2023 to 2024 (validation). The primary outcome was bacteremia. Potential predictors of bacteremia were derived from a literature review and experts' consensus. We used Classification and Regression Tree models to derive a highly sensitive clinical decision rule. The validity was assessed by measuring the proportion of children with true bacteremia classified at high or moderate risk by the clinical decision rule (sensitivity) and the proportion of contaminants classified at low risk (specificity). The clinical utility was measured by comparing the clinical decision rule to the treating physician's management. RESULTS: A total of 747 children, including 368 cases of bacteremia were included in the derivation (total 574; 285 bacteremia) and validation (total 173; 83 bacteremia) cohorts. The clinical decision rule classifies children into 3 categories (high, moderate, and low risk) based on 4 criteria. It demonstrated a sensitivity of 99% (95% confidence interval [CI] 94 to 100) and a specificity of 60% (95% CI 50 to 70) in the validation cohort. Using the clinical decision rule among the 43 children initially discharged in the validation cohort would decrease the number of hospitalizations from 34 to 21 without missing a true bacteremia. CONCLUSION: We created a very sensitive clinical decision rule to identify bacteremia in children with positive blood culture. Adopting this clinical decision rule could significantly impact health system resources.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".