Predicting Severe Short-Term Neurologic Outcomes in Human Parechovirus Meningoencephalitis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Human parechovirus (PeV) is an increasingly recognized cause of meningoencephalitis (ME) in infants. The US 2022 outbreak provided opportunity to analyze the clinical presentation and predictors of severe disease in affected infants. METHODS: We conducted a multicenter retrospective review of infants diagnosed with PeV ME during the outbreak. We examined demographics, clinical features, laboratory findings, and neuroimaging results. Logistic regression was used to identify predictors of complicated disease and abnormal brain magnetic resonance imaging (MRI). Complicated disease was defined as requiring intensive care or findings of an abnormal brain MRI or electroencephalogram. RESULTS: 139 infants had PeV ME. The median age was 19 days. Fever was the most common presenting symptom (89.2%) and was associated with uncomplicated disease and normal MRI. A total of 42 (30.2%) infants had complicated disease. Hypothermia (36.5% vs 5.1%), somnolence (38.1% vs 13.4%), poor feeding (76.1% vs 47.4%), hemodynamic instability (28.5% vs 3%), seizures (57.1% vs 4.1%), apnea (40.4% vs 0%), hypoglycemia (16.6% vs 1%), mechanical ventilation (23.8% vs 0%), and inotropic support (11.9% vs 0%) were associated with complicated disease. Younger age and seizures were predictors of abnormal MRI on multivariable analysis (adjusted odds ratio, 0.92 [0.48-0.99] and 40.1 [3.49-460.7], respectively). Laboratory findings, including cerebrospinal fluid indices, were rarely abnormal. CONCLUSION: Despite nonspecific symptoms on presentation and normal laboratory values, PeV can cause complicated disease, requiring clinicians to maintain high suspicion for this infection. We suggest PeV evaluation in workup of infant sepsis cases, neuroimaging in patients at high risk, and long-term developmental follow-up.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".