An improved clinical prediction rule for identifying neonatal bacterial meningitis: a multicenter cohort study
Bibliographic record
Abstract
Background: To refine the bacterial meningitis (BM) score by improving its predictability of neonatal BM. Methods: A multicenter, ambispective cohort study was conducted in China, comprising 9 hospitals (retrospective cohort: January 2001 to December 2017; prospective cohort: January 2018 to August 2019). Of 3,504 eligible full-term neonates, 475 neonates with cerebrospinal fluid (CSF) pleocytosis were included. Based on the receiver operating characteristic (ROC) curve and logistic regression analyses, the BM score was refined by changing the thresholds of CSF protein level and the CSF absolute neutrophil count (ANC), and removing some variables (the peripheral blood ANC and a history of seizure before or at the time of presentation). Results: Of 475 neonates, 94 (19.8%) had BM. Based on the refined BM score, neonates with none of the following high-risk predictors were classified as being at very low risk for BM: CSF protein level ≥1,650 mg/L, CSF ANC ≥84×106 cells/L, and positive CSF Gram stain result. The refined score showed 100% sensitivity in identifying BM and much higher specificity compared to that for the BM score (70.9% vs. 19.4%). Conclusions: The refined BM score effectively identifies neonatal BM, and further studies are required to confirm our findings in prospective studies.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".