Predicting the Outcome of Neonatal Bacterial Meningitis
Bibliographic record
Abstract
OBJECTIVE: To build predictive models of severe adverse outcome at various times in the course of neonatal bacterial meningitis. STUDY DESIGN: Retrospective cohort study with follow-up to a minimum age of 1 year of term and near-term infants, admitted between 1979 and 1998 to a regional tertiary care center. Predictors of adverse outcome detectable at 1 year of age (death or moderate or severe neurosensory impairment) were identified by univariate analysis. Independent predictors of adverse outcome were identified by multivariate analysis. Predictive tree models were constructed at 12, 24, 48, and 96 hours after admission and at discharge. RESULTS: Of 101 infants admitted with definitive bacterial meningitis, 13 died and 17 had moderate or severe disability at 1 year of age. Outcomes are known for all patients, to 1 year of age. Twelve hours after admission the important predictors of adverse outcome were presence of seizures, presence of coma, use of inotropes, and leukopenia (sensitivity: 68%; specificity: 100%). At 96 hours the predictors were seizure duration of >72 hours, presence of coma, use of inotropes, and leukopenia (sensitivity: 88%; specificity: 99%). CONCLUSIONS: Most infants at risk for adverse outcome can be identified within 12 hours of admission. Duration of seizures for >72 hours, presence of coma, use of inotropes, and leukopenia were the most important predictors of adverse outcome. Although these models have good predictive accuracy, they need to be validated in a contemporary cohort in large multicenter 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".