Markers for bacterial infection in children with fever without source
Bibliographic record
Abstract
OBJECTIVES: To compare the diagnostic properties of procalcitonin (PCT), C reactive protein (CRP), total white blood cells count (WBC), absolute neutrophil count (ANC) and clinical evaluation to detect serious bacterial infection (SBI) in children with fever without source. DESIGN: Prospective cohort study. SETTING: Paediatric emergency department of a tertiary care hospital. PARTICIPANTS: Children aged 1-36 months with fever and no identified source of infection. INTERVENTION: Complete blood count, blood culture, urine analysis and culture. PCT and CRP were also measured and SBI probability evaluated clinically with a visual analogue scale before disclosing tests results. Outcome measure Area under the curves (AUC) of the receiver operating characteristic curves. RESULTS: Among the 328 children included in the study, 54 (16%) were diagnosed with an SBI: 48 urinary tract infections, 4 pneumonias, 1 meningitis and 1 bacteraemia. The AUC were similar for PCT (0.82; 95% CI 0.77 to 0.86), CRP (0.88; 95% CI 0.84 to 0.91), WBC (0.81; 95% CI 0.76 to 0.85) and ANC (0.80; 95% CI 0.75 to 0.84). The only statistically significant difference was between CRP and ANC (Δ AUC 0.08; 95% CI 0.01 to 0.16). It is important to note that all the surrogate markers were statistically superior to the clinical evaluation that had an AUC of only 0.59 (95% CI 0.54 to 0.65). CONCLUSION: The study data demonstrate that CRP, PCT, WBC and ANC had almost similar diagnostic properties and were superior to clinical evaluation in predicting SBI in children aged 1 month to 3 years.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".