The Case Against Routine Electroencephalography in Specific Language Impairment
Bibliographic record
Abstract
BACKGROUND: Specific language impairment is a primary developmental language disorder in which language is impaired disproportionately to other developmental domains. Electroencephalography is often conducted in the medical investigation of a child with specific language impairment; however, at present, there is uncertainty regarding necessary testing using electroencephalography. METHODS: The cases of 111 children with the diagnosis of specific language impairment over a 10-year interval, who also underwent electroencephalography, were systematically reviewed in a retrospective manner. Children with a history of previous afebrile seizures, acquired language delay, or documented language regression, developmental delay, hearing loss, coexisting autistic features, and known central nervous system disorders were excluded. RESULTS: The majority (76%) of the children were boys. Thirty-five (31.5%) children had abnormal electroencephalography results, including 7 (6.3%) children with epileptiform activity. This is higher than the prevalence rate of epileptiform activity in a historical cohort of 3726 (3.54%) children but not statistically significant. The epileptiform activity was deemed active in only 3 of 7 patients and was not related to the specific type of language delay observed. CONCLUSIONS. Although abnormal electroencephalographic activity is seen frequently in children with specific language impairment, epileptiform activity is rare and without apparent impact on clinical care. Awake electroencephalography does not seem to be useful in the routine diagnostic evaluation of young children with specific language impairment, although further investigations of both wake and sleep electroencephalography in this homogenous population must be conducted before definitive recommendations can be made.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".