Reduced Cortical Thickness in Children with New-Onset Seizures: Fig 1.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Children with new-onset seizures may have antecedent neurobiologic alterations that predispose them to developing seizures. Our aim was to evaluate hippocampal and thalamic volumes and lobar cortical thickness of children with new-onset seizures. MATERIALS AND METHODS: Twenty-nine children with new-onset seizures and normal MR imaging findings were recruited. Ten patients had generalized seizures, 19 had partial seizures, and 15 were on antiepileptic medications. Twenty-three age-matched healthy controls were also recruited. Hippocampal and thalamic volumes and lobar cortical thickness, including frontal, medial temporal, lateral temporal, parietal, cingulate, and occipital cortical thickness, were assessed by using volumetric T1-weighted imaging and were compared between patients and controls. RESULTS: There were no significant differences in hippocampal and thalamic volumes of patients with new-onset seizures, including the subgroups with generalized and partial seizures and those on and off antiepileptic medications, compared with controls (P > .01). There was significant reduction in cortical thickness in right cingulate (P = .004), right medial temporal (P = .006), and left frontal (P = .007) cortices in patients with new-onset seizures. Patients with generalized seizures did not demonstrate a significant reduction in cortical thickness (P > .01). Patients with partial seizures demonstrated a significant reduction in cortical thickness in the right frontal (P = .008), right parietal (P = .003), and left frontal (P = .007) cortices. There were no significant differences in cortical thickness among patients on or off antiepileptic medications (P > .01). CONCLUSIONS: We found reduced cortical thickness in children with new-onset seizures. Further studies are necessary to elucidate the neurobiologic relevance of these structural changes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".