EPILEPTIFORM ABNORMALITIES IN PATIENTS WITH DEVELOPMENTAL LANGUAGE IMPAIRMENT
Bibliographic record
Abstract
Objectives: Developmental language impairment (DLI) is a developmental disorder of language not associated with global developmental delay. Electroencephalograms (EEG) are frequently performed on these children to rule out possible epileptic abnormalities and underlying etiologies. The objective of this study was to determine the proportion and type of EEG abnormalities in patients with DLI and compare this to a previously published control cohort. Methods: This was a systematic retrospective review of patients presenting with language delay to a single pediatric neurologist. Only those patients presenting with language delay, or behaviour abnormalities with language delay were included in this study. Patients with language regression, autistic tendencies, groß motor delays, hearing loss, or previous non-febrile seizures were excluded. Logistic regression analysis was done to reveal predictors of an abnormal or epileptic EEG. Results: 129 patients were included in this study. 75% of patients referred were male. 18.5% had associated fine motor abnormalities. 54% had a mixed language disorder, and 42% had an expressive language disorder alone. 78.3% of patients had EEGs performed. Of the patients who had EEGs, 6 (5.9%) of patients had epileptic activity: 3 in the posterior quadrants, 1 in the left temporal region, 1 generalized, and 1 multifocal. Only one patient was treated with an antiepileptic medication (carbamezepine). 23.3% had non-epileptic abnormalities with the majority having a mild or moderate diffuse disturbance. There were no predictive factors for an abnormal or epileptic EEG found using logistic regression analysis. Conclusion: In comparison to a published control cohort of normal school aged children where 3.5% had epileptiform findings on EEG, our study demonstrated that in patients with DLI, 5.6% had an epileptiform EEG of which only one child received treatment. Overall, EEG is not helpful to determine the underlying etiology of DLI even if epileptic abnormalities are discovered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".