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 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 | 0.166 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.028 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".