Neuroethical issues related to the use of brain imaging: Can we and should we use brain imaging as a biomarker to diagnose chronic pain?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article. aDivision of Brain, Imaging and Behaviour – Systems Neuroscience, Toronto Western Research Institute, University Health Network, Toronto, ON, Canada bInstitute of Medical Science, University of Toronto, Toronto, ON, Canada cDepartment of Surgery, University of Toronto, Toronto, ON, Canada dNeuroethics Research Unit, Institut de recherches cliniques de Montréal, Department of Medicine and Department of Social and Preventive Medicine, Université de Montréal, Montréal, QC, Canada eDepartments of Neurology and Neurosurgery, Medicine & Biomedical Ethics Unit, McGill University, Montréal, QC, Canada fPain Management Service, University Hospitals of Leicester, Leics, UK *Corresponding author. Address: Division of Brain, Imaging and Behaviour – Systems Neuroscience, Toronto Western Research Institute, 399 Bathurst Street, Room MP14-306, Toronto, ON, Canada M5T 2S8. Tel.: +1 416 603 5662; fax: +1 416 603 5745. E-mail address:[email protected]
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.
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 | 0.010 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it