Chromosomal abnormalities and schizophrenia
Bibliographic record
Abstract
Schizophrenia is a common and serious psychiatric illness with strong evidence for genetic causation, but no specific loci yet identified. Chromosomal abnormalities associated with schizophrenia may help to understand the genetic complexity of the illness. This paper reviews the evidence for associations between chromosomal abnormalities and schizophrenia and related disorders. The results indicate that 22q11.2 microdeletions detected by fluorescence in-situ hybridization (FISH) are significantly associated with schizophrenia. Sex chromosome abnormalities seem to be increased in schizophrenia but insufficient data are available to indicate whether schizophrenia or related disorders are increased in patients with sex chromosome aneuploidies. Other reports of chromosomal abnormalities associated with schizophrenia have the potential to be important adjuncts to linkage studies in gene localization. Advances in molecular cytogenetic techniques (i.e., FISH) have produced significant increases in rates of identified abnormalities in schizophrenia, particularly in patients with very early age at onset, learning difficulties or mental retardation, or dysmorphic features. The results emphasize the importance of considering behavioral phenotypes, including adult onset psychiatric illnesses, in genetic syndromes and the need for clinicians to actively consider identifying chromosomal abnormalities and genetic syndromes in selected psychiatric patients.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".