Pattern of Brain Injury Predicts Long-Term Epilepsy Following Neonatal Encephalopathy
Bibliographic record
Abstract
Objective: To determine if patterns of hypoxic-ischemic brain injury on magnetic resonance imaging (MRI) in term newborns predict subsequent childhood epilepsy. Methods: This retrospective cohort study includes term newborns with encephalopathy (n = 181) born between 2004-2012 and admitted to British Columbia Children’s Hospital. MRI was performed between 3 and 5 days of age. The predominant patterns of hypoxic-ischemic injury were classified as Normal, Watershed, Basal Nuclei, Total, and Focal-Multifocal. Lesions in hippocampus, motor and occipital cortex were noted. Results: Of 181 newborns, 166 (92%) survived the neonatal period, and 132 (80%) had follow-up with a median duration of 61 months (IQR: 28–95). Twenty-three children (17%) developed epilepsy. A higher proportion with Watershed, Basal Nuclei, or Total patterns developed epilepsy ( P < .001). Injury to motor cortex, hippocampus, and occipital lobe ( P < .01) were independent risk factors for epilepsy. In the adjusting logistic model, Watershed (odds ratio = 16.0, 95% CI [1.3, 197.2], P = .03) and Basal Nuclei injury (odds ratio = 19.4, 95% CI [1.9, 196.3], P = .01) remained independent risk factors. Therapeutic hypothermia did not alter these associations. Severity of brain injury and recurrent neonatal seizures are other clinical risk factors. Significance: In term newborns with hypoxic-ischemic encephalopathy, the predominant pattern of Watershed and Basal Nuclei injury are valuable predictors for development of epilepsy in later childhood.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".