Etiologic Yield of Autistic Spectrum Disorders: A Prospective Study
Bibliographic record
Abstract
At present, the etiologic yield in community-derived samples of young children with an autistic spectrum disorder is not known. To address this question, all young children (under 5 years of age) referred for an initial assessment to ambulatory pediatric neurology or developmental pediatric clinics at a tertiary university center over an 18-month period for a suspected developmental delay were prospectively identified. Specific diagnostic testing was left to the discretion of the evaluating physician. In all, 50 children with an autistic spectrum disorder were assessed. Detailed history or physical examination was informative with respect to suggesting the possibility of an underlying etiology in a minority (10/50,20%). Genetic studies (FMR-1, karyotype), electroencephalography (EEG), and neuroimaging were carried out in a majority (42/50, 34/50, and 33/50, respectively) of the children, for the most part on a screening rather than an indicated basis (31/42, 34/34, and 28/33, respectively). Etiologic yield was low (1/50, 2%), with only a single child identified with a possible Landau-Kleffner variant on sleep EEG tracing. The results suggest an evaluation paradigm with reference to etiologic determination for young children with autistic spectrum disorder that does not presently justify metabolic or neuroimaging on a screening basis. Recurrence risk and treatment implications, however, suggest that strong consideration be given to genetic (FMR-1, karyotype) testing and EEG study despite a relatively low yield.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".