Developmental psychopathology: The role of structural variation in the genome
Bibliographic record
Abstract
A wide range of developmental disorders present with characteristic psychopathologies and behaviors, with diagnoses including, inter alia, cognitive disorders and learning disabilities, epilepsies, autism, and schizophrenia. Each, to varying extent, has a genetic component to etiology and is associated with cytogenetic abnormalities. Technological developments, particularly array-based comparative genome hybridization and single nucleotide polymorphism chips, has revealed a wide range of rare recurrent and de novo copy number variants (CNVs) to be associated with disorder and psychopathology. It is surprising that many apparently similar CNVs are identified across two or more disorders hitherto considered unrelated. This article describes the characteristics of CNVs and current technological restrictions that make accurately identifying small events difficult. It summarizes the latest discoveries for individual diagnostic categories and considers the implications for a shared neurobiology. It examines likely developments in the knowledge base as well as addressing the clinical implications going forward.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".