The CAMH Neuroinformatics Platform: A Hospital-Focused Brain-CODE Implementation
Bibliographic record
Abstract
Understanding the human brain in both healthy function and in the context of psychiatric illness presents a formidable technical and analytic challenge for medical researchers. Multi-modal data, including medical imaging, molecular and clinical measures provide lenses through which the brain’s structure, function, expression and behavioral presentation can be studied. While directed integration of complementary information promises to accelerate discovery and identify cross-modal biomarkers for stratification, diagnosis and treatment, such approaches, require stringent data standardization and are often computationally demanding, compounded by increased data volumes, statistical power and sample size requirements. To realize the potential of multi-modal data integration toward the study of mental illness, the Center for Addiction and Mental Health (CAMH) constructed a centralized data capture, visualization and analytics environment – the CAMH Neuroinformatics Platform – based on the Ontario Brain Institute (OBI) Brain-CODE platform, enabling the curation of a standardized, consolidated psychiatric hospital-wide research dataset, directly coupled to high performance computing resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".