Abstract TMP104: Stroke in Paediatric Bacterial Meningitis
Bibliographic record
Abstract
Introduction: Bacterial meningitis is a severe intracranial infection with a high complication rate. Neurological morbidity relates to parenchymal involvement where vascular mechanisms and stroke may predominate. Here, we systematically identified all neonates and children with bacterial meningitis and compared those with and without MRI-confirmed stroke. We hypothesized that patterns of stroke would correlate with causative organism and neurological outcome. Methods: Population-based (2.2 million), ten-year retrospective (2002-2012) cross-sectional study in Southern Alberta, Canada. Inclusion criteria were: (1) age from newborn (including prematurity) to 18 years, (2) brain MRI including DWI during admission, and (3) laboratory confirmed acute bacterial meningitis. Demographics, clinical presentations, risk factors, and laboratory findings were extracted. Original imaging was blindly reviewed and classified for stroke and injury pattern. Outcomes were extrapolated to the Pediatric Stroke Outcome Measure (PSOM). Results: Twenty-four patients had confirmed bacterial meningitis and acute MRI (6 neonates, 83% male, and 18 children, mean age 3.14+/-3.9 years, 67% male). Arterial ischemic stroke was confirmed in 9/24 (38%). Strokes were often multifocal (89%), particularly in neonates (100%). Subjects with stroke were more likely to have longer duration of illness prior to presentation, meningismus, prolonged fever, seizures, and new focal neurological signs. The most common organisms were S. pneumonia in older children (44%) and group B streptococcus in neonates (33%). Three patients received steroids, 3 ASA and 1 heparin and ASA. Mortality was 22% in children with stroke and zero in those without (p=0.16). Survivors with stroke were more likely to have motor deficit (71% vs. 25%, p = 0.07), cognitive deficit (75% vs. 38%, p = 0.5), and speech deficit (60% vs. 10%, p = 0.08). Conclusions: More than a third of children with bacterial meningitis warranting MRI have stroke. Associations include longer duration of illness, meningismus, focal neurological signs and common causative organisms. Stroke was associated with higher mortality and morbidity, warranting consideration of increased MRI screening and new approaches to treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".