Neonatal Encephalopathy With Group B Streptococcal Disease Worldwide: Systematic Review, Investigator Group Datasets, and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Neonatal encephalopathy (NE) is a leading cause of child mortality and longer-term impairment. Infection can sensitize the newborn brain to injury; however, the role of group B streptococcal (GBS) disease has not been reviewed. This paper is the ninth in an 11-article series estimating the burden of GBS disease; here we aim to assess the proportion of GBS in NE cases. METHODS: We conducted systematic literature reviews (PubMed/Medline, Embase, Latin American and Caribbean Health Sciences Literature [LILACS], World Health Organization Library Information System [WHOLIS], and Scopus) and sought unpublished data from investigator groups reporting GBS-associated NE. Meta-analyses estimated the proportion of GBS disease in NE and mortality risk. UK population-level data estimated the incidence of GBS-associated NE. RESULTS: Four published and 25 unpublished datasets were identified from 13 countries (N = 10436). The proportion of NE associated with GBS was 0.58% (95% confidence interval [CI], 0.18%-.98%). Mortality was significantly increased in GBS-associated NE vs NE alone (risk ratio, 2.07 [95% CI, 1.47-2.91]). This equates to a UK incidence of GBS-associated NE of 0.019 per 1000 live births. CONCLUSIONS: The consistent increased proportion of GBS disease in NE and significant increased risk of mortality provides evidence that GBS infection contributes to NE. Increased information regarding this and other organisms is important to inform interventions, especially in low- and middle-resource contexts.
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".