Neuroethical issues related to the use of brain imaging: Can we and should we use brain imaging as a biomarker to diagnose chronic pain?
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]
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".