Cathy Barr: Genetics and neurobiology of childhood psychiatric and cognitive disorders
Bibliographic record
Abstract
Cathy Barr completed her Ph.D. in molecular biology at the University of Texas, Graduate School of Biomedical Sciences (M.D. Anderson Cancer Center) in Houston, Texas, followed by postdoctoral training in the genetics of complex behaviors at Yale University and the Hospital for Sick Children in Toronto. Currently serving as a Senior Scientist at both the Hospital for Sick Children and the Krembil Research Institute (University Health Network) and as a Professor in the Departments of Psychiatry and Physiology at The University of Toronto, Dr. Barr investigates the genetic foundations of behavior, cognition, and psychiatric disorders with established genetic predispositions. Her research mainly focuses on childhood-onset conditions, including depression, attention-deficit/hyperactivity disorder, reading disabilities, and Tourette syndrome, with special emphasis on understanding shared risk factors across disorders – a critical area given that children with neurodevelopmental disorders face a fivefold increased risk of depression. Through innovative approaches, Dr. Barr and her research team have successfully identified risk-contributing genes. They are investigating how DNA variations in these genes influence gene function and neural cell behavior. In this Genomic Press Interview, she generously shares insights from her groundbreaking research into the genetic underpinnings of childhood psychiatric and neurodevelopmental disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".