Elevated rates of schizophrenia in a familial sample with mental illness and intellectual disability
Bibliographic record
Abstract
BACKGROUND: It is unknown whether intellectual disability (ID) is more familially related to psychotic mood disorders or schizophrenia. L. S. Penrose's large sample of families with two or more members admitted to psychiatric hospitals provided a unique opportunity to investigate the familial relationship between mild ID, schizophrenia and psychotic affective disorders. METHOD: There were 183 affected relative pairs comprising probands with mild ID (95 male, 88 female) and their first or second degree relatives with schizophrenia or psychotic affective disorder. RESULTS: There were nearly twice as many relatives with a diagnosis of schizophrenia (n = 121) as relatives with affective disorders (n = 62) among the intellectually impaired probands. This excess of schizophrenia was statistically significant, even after accounting for the increased risk of hospitalization for schizophrenia (P = 0.005), and was fairly constant across the different relative types. First-degree relatives with either mental illness were more likely to be parents (n = 77) than siblings (n = 51) or children (n = 3), but there was no excess of mother-son pairs. CONCLUSIONS: These results suggest a stronger familial relationship of ID with schizophrenia than psychotic affective disorder, and lend some support to the neurodevelopmental hypothesis of schizophrenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".