Predictors of Bacterial Meningitis in the Era After Haemophilus influenzae
Bibliographic record
Abstract
OBJECTIVE: To determine if, in the era after Haemophilus influenzae type b, the cerebrospinal fluid (CSF) white blood cell (WBC) count can be safely used to stratify children suspected of having bacterial meningitis into low- and high-risk groups. DESIGN: Retrospective analysis of CSF samples. SETTING: Tertiary care pediatric center in Toronto, Ontario, between January 1, 1992, and October 1, 1996. PATIENTS: All CSF samples collected on children aged 2 months to 17 years were included. The final database consisted of 1617 atraumatic samples from children without prior neurologic or immunologic disease who underwent a lumbar puncture to assess the possibility of community-acquired bacterial meningitis. MAIN OUTCOME MEASURES: The predictive values of CSF WBC count, differential, protein, and glucose. RESULTS: There were 44 cases of bacterial meningitis. Five had 3 CSF WBCs per microliter or less, and 6 had 4 to 30 CSF WBCs per microliter. The negative predictive value of CSF specimens with 30 WBCs per microliter or less for bacterial meningitis was 99.3%. Cerebrospinal fluid samples with greater than 30 WBCs per microliter had a likelihood ratio for bacterial meningitis of 10.3 (95% confidence interval, 8.0-13.1) and a positive predictive value of 22.3%. Other significant predictors of bacterial meningitis included age, CSF glucose, protein, gram stain, CSF-serum glucose ratio, and peripheral blood band count. CONCLUSIONS: Given the occurrence of bacterial meningitis in children in the absence of CSF pleocytosis, other factors should be considered when managing children with suspected bacterial meningitis. Children older than 6 months with 30 CSF WBCs per microliter or less are at low risk for bacterial meningitis. If clinically stable and without other laboratory markers of bacterial meningitis, hospital admission and empiric antibiotic therapy may be unwarranted.
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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 | 0.000 | 0.000 |
| 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".