Episodic psychiatric disorders in teenagers with learning disabilities with and without autism
Bibliographic record
Abstract
BACKGROUND: Mental health problems in people with learning disabilities and autism are poorly understood. AIMS: To investigate the prevalence of episodic psychiatric disorders in a sample of teenagers with learning disabilities with and without autism. METHOD: Teenagers with learning disabilities living in one geographical area were identified. Those with autism were matched to those without. A semi-structured investigator-based interview linked to Research Diagnostic Criteria was used to assess prevalence and type of episodic disorders. RESULTS: Significantly more individuals with autism had a lifetime episodic disorder, most commonly major depression. Two individuals with autism had bipolar affective disorder. Other episodic disorders with mood components and behaviour change were also evident, as were unclassifiable disorders characterised by complex psychiatric symptoms, chronicity and general deterioration. Antipsychotics and stimulants were most frequently prescribed; the former associated with episodic disorder, the latter with autism. CONCLUSIONS: Teenagers with learning disabilities and autism have higher rates of episodic psychiatric disorders than those with learning disabilities alone.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".