Incidental neurodevelopmental episodes in the etiology of schizophrenia: An expanded model involving epigenetics and development
Bibliographic record
Abstract
Epidemiological data favors genetic predisposition for schizophrenia, a common and complex mental disorder in most populations. Search for the genes involved using candidate genes, positional cloning, and chromosomal aberrations including triplet repeat expansions have established a number of susceptibility loci and genomic sites but no causal gene(s) with a proven mechanism of action. Recent genome-wide gene expression studies on brains from schizophrenia patients and their matched controls have identified a number of genes that show an alteration in expression in the diseased brains. Although it is not possible to offer a cause and effect association between altered gene expression and disease, such observations support a neurodevelopmental model in schizophrenia. Here, we offer a mechanism of this disease, which takes into account the role of developmental noise and diversions of the neural system. It suggests that the final outcome of a neural developmental process is not fixed and exact. Rather it develops with a variation around the mean. More important, the phenotypic consequence may cross the norm as a result of fortuitous and/or epigenetic events. As a result, a normal genotype may develop as abnormal with a disease phenotype. More important, susceptible genotypes may have reduced penetrance and develop as a normal phenocopy. The incidental episodes in neurodevelopment will explain the frequency of schizophrenia in most populations and high discordance of monozygotic twins.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".