S11. BRAIN ABNORMALITIES ASSOCIATED WITH DISEASE-SPECIFIC AND GENETIC RISK FACTORS FOR SCHIZOPHRENIA
Bibliographic record
Abstract
Schizophrenia is a severe disorder affecting approximately 1% of the population. The disorder is associated with symptoms such as false perceptions and beliefs and disturbances in affect and language production. In 2004 the total direct healthcare and non-healthcare cost in Canada was estimated at $2 billion, with an additional productivity loss estimated at $5 billion. I will present a program of research into disease-specific and genetic risk factors associated with structural and functional brain abnormalities, including morphology (amount of grey matter), structural connectivity (amount of white matter integrity), and brain functioning (amount of brain activity) in individuals with schizophrenia, their family members, and community controls using magnetic resonance imaging. As healthy relatives share genes with their affected family member, but do not share the disease process, abnormalities present in relatives are likely associated with the genes for schizophrenia. Evidence was found for disease-specific, genetic risk and compensatory brain mechanisms associated with schizophrenia that were complementary between the results from brain morphology, structural connectivity, and brain functioning. Isolating the biological and genetic basis of these deficits could ultimately aid in developing novel psychosocial and pharmacological treatments to facilitate improved day-to-day functioning in schizophrenia.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.087 | 0.009 |
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".