Functional cognitive and language outcomes after cerebral hemispherectomy for hemimegalencephaly
Bibliographic record
Abstract
OBJECTIVE: Hemispheric surgeries are an effective treatment option to control seizures for children with hemimegalencephaly (HME); however, not enough is known about their cognitive outcomes. This study aimed to delineate the cognitive and language outcomes after hemispherectomy for HME and identify the clinical characteristics associated with cognition and language. METHODS: Data came from the Global Pediatric Epilepsy Surgery Registry, a patient-driven web-based registry for epilepsy surgery. We focused on children's functional status, assessed through parent-reports of cognitive and language skills. Parents also reported on their satisfaction with surgery, their child's quality of life, and various demographic, clinical, and surgery characteristics. RESULTS: Parents of 45 children (40% female) participated. Children were aged 2.6 (SD 6.5) months at seizure onset, 10.8 (SD 12.7) months at hemispherectomy, and 8.7 (SD 4.8) years at follow-up, at which point 68% were seizure-free. We found that at follow-up, 43% had average or mildly impaired cognition, 26% could speak age appropriately, and 21% had satisfactory reading skills. A total of 55%, 43%, and 17% of children first babbled, spoke their first words, and started speaking in sentences at an age-appropriate period, respectively. Children who had undergone a right hemisphere resection and those who were older at epilepsy onset were more likely to have better cognitive and language outcomes. SIGNIFICANCE: Children with HME have delayed language milestones and continue to require significant language and literacy support long-term after cerebral hemispherectomy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".