Association of neonatal inflammatory markers and perinatal stroke subtypes
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between neonatal inflammatory cytokines and perinatal stroke using a systems biology approach analyzing serum and blood-spot cytokines from 47 patients. METHODS: This was a population-based, controlled cohort study with prospective and retrospective case ascertainment. Participants were recruited through the Alberta Perinatal Stroke Project. Stroke was classified as neonatal arterial ischemic stroke (NAIS), arterial presumed perinatal ischemic stroke (APPIS), or periventricular venous infarction (PVI). Biosamples were stored blood spots (retrospective) and acute serum (prospective). Controls had comparable gestational and maternal ages. Sixty-five cytokines were measured (Luminex). Hierarchical clustering analysis was performed to create heat maps. The Fisher linear discriminant analysis was used to create projection models to determine discriminatory boundaries between stroke types and controls. RESULTS: A total of 197 participants were analyzed (27 with NAIS, 8 with APPIS, 12 with PVI, 150 controls). Cytokines were quantifiable with quality control measures satisfied (standards testing, decay analysis). Linear discriminant analysis had high accuracy in using cytokine profiles to separate groups. Profiles in participants with PVI and controls were similar. NAIS separation was accurate (sensitivity 77%, specificity 97%). APPIS mapping was also distinguishable from NAIS (sensitivity 86%, specificity 99%). Classification tree analysis generated similar diagnostic accuracy. CONCLUSIONS: Unique inflammatory biomarker signatures are associated with specific perinatal stroke diseases. Findings support an acquired pathophysiology and suggest the possibility that at-risk pregnancies might be identified to develop prevention strategies. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that differences in acute neonatal serum cytokine profiles can discriminate between patients with specific perinatal stroke diseases and controls.
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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 | 0.003 |
| 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 | 0.000 | 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".