Patient Registries in Cognitive Neuroscience Research: Advantages, Challenges, and Practical Advice
Bibliographic record
Abstract
Neuropsychological work is the historical foundation of cognitive neuroscience and continues to be an important method in the study of the neural basis of human behavior, complementing newer techniques for investigating brain structure-function relationships in human subjects. Recent advances in neuroimaging, statistics and information management provide powerful tools to support neuropsychological research. At the same time, changing ethical requirements and privacy concerns impose increasingly high standards on the procedures used to recruit research participants, and on subsequent data management. Shared, centrally managed research registries provide a framework for facilitating access to this method for nonclinicians, addressing ethical concerns, streamlining recruitment and screening procedures, and coordinating subsequent research contacts and data storage. We report the experience of two such registries: the patient database of the Center for Cognitive Neuroscience at the University of Pennsylvania, and the Cognitive Neuroscience Research Registry at McGill University.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".