Molecular Genetics of the Psychosis Phenotype
Bibliographic record
Abstract
OBJECTIVE: Relative to recent successes in elucidating the genetic mechanisms associated with complex diseases, including macular degeneration, diabetes mellitus, type 2, heart disease, and cancer, molecular genetic approaches to psychiatric illness have met with more limited success. While factors such as small allelic effects, allelic heterogeneity, and variation in population substructure have received considerable attention in attempt to explain the paucity of significant results in psychiatric genetics, significantly less focus has been directed toward phenotypic factors. METHOD: Data derived from molecular genetic studies of the psychosis phenotype in patients with a range of psychiatric illnesses are reviewed. RESULTS: Available data suggest that genes do not respect the boundaries of the current diagnostic system but may confer risk for symptom-based phenotypic variation that traverses those boundaries. CONCLUSIONS: Molecular genetic studies offer convincing evidence for a relation between genetic variation and symptom-based phenotypic variation within psychiatric illness. These data may provide novel insights into the pathophysiology of schizophrenia and other related disorders. The exploration of relations between genetic variation and symptom variation that traverses traditional diagnostic boundaries may ultimately lead to more refined classification systems that more closely reflect the genetic etiology of psychiatric illness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".