Clinical predictors of poor outcome of bacterial meningitis in infants less than 90 days: a systematic review
Bibliographic record
Abstract
Background: bacterial meningitis (BM) is more common in infants than at any other time in life and remains a devastating disease with considerable risk of death and morbidity. This article aims to gather the currently available evidence to perform a systematic review of clinical factors that may predict or be associated with BM death and/or sequelae in infants < 90 days of age. Methods: The Medline/PubMed, Cochrane Library and Embase databases were systematically searched for prognostic studies that described risk factors for mortality and sequelae in infants aged <90d with BM. The databases were searched from the beginning of the database to December 31st, 2022.The quality of cohort studies was assessed by the Newcastle-Ottawa Scale (NOS). The quality of cross-section studies was assessed by the Agency for Healthcare Research and Quality (AHRQ). A systematic review was undertaken to ascertain the prognostic factors proven to be noteworthy. Results: Of the 1,431 studies retrieved, 20 were eligible for the final analysis including 11 cohort and 9 cross-sectional studies were identified. Four risk factors predicting poor outcome were mentioned mostly in those studies, including prematurity or low birth weight (LBW), seizures, coma, and elevated CSF protein. But only preterm, coma and elevated CSF protein were identified by multivariate analyses in more than one study. Conclusions: This study demonstrates several potential predictive factors to the poor outcomes of BM in infant. But with large heterogeneity, these predictors should be evaluated by further well-designed prospective studies. Systematic Review Registration: https://www.crd.york.ac.uk/, identifier CRD42017074949.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".