D.03 Down syndrome: clinical and EEG correlates during development
Bibliographic record
Abstract
Background: Down syndrome (DS) is the primary genetic cause of mental retardation and seizures are present in an estimated 5-13% of cases. One-third of seizures in DS are infantile spasms (IS). Hypsarrythmia (HS) is the cardinal electroencephalogram (EEG) feature of IS and has been found to affect cognition; however, its effect on DS patients is inconclusively reported. This study assesses the correlation of HS with cognitive outcomes in DS using the largest sample size to date. Methods: Retrospective study of medical records of children with DS [0-18yrs] at SickKids Hospital in Toronto, from 1990-2013. Seizure history, EEG findings, comorbities, and pharmacological treatments were identified. Developmental outcomes were also assessed from physician comments on motor, verbal and cognitive abilities. The cognitive outcomes of DS patients with and without HS were compared. Results: 70 [male=40] patients with DS and seizures were included. Among 31 (44.2%) patients with DS and IS, 27 had HS. Chi-square analysis showed a significant difference [P=0.007] in prevalence of severe developmental delay in patients with IS and HS versus all other seizure types. Conclusions: The developmental outcome of patients with Down syndrome appears to worsen when IS and HS had occurred in the first year of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.004 | 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".