Neuroethical issues in adopting brain imaging for personalized chronic pain management: Attitudes of people with lived experience of chronic pain
Bibliographic record
Abstract
Background: Pain is an individual and subjective experience that places a burden on individuals to convince others they have pain. Brain imaging technologies can potentially inform pain management but raise neuroethical questions. Aims: We examined the degree of endorsement and concerns of adults in Canada with chronic pain regarding the use of brain imaging to detect and treat chronic pain in six areas: new brain imaging technologies, brain data privacy, stigma, treatment, objective representations of pain, and dismissing pain self-reports. Results: An online survey was completed by 349 Canadian adults living with chronic pain. Most respondents were open to using brain imaging for diagnostics, prediction, and therapeutic decision making (>90%). More than half of respondents felt that a brain scan would give them more confidence in their diagnosis and treatment plans and that health care providers would be more likely to believe they had chronic pain. However, they worried that brain scans could be used to dismiss their pain self-report. Most respondents felt there were policies to protect their brain data, but 40% were concerned about privacy and brain scan use against them by their employers/insurers. Although most respondents felt that a brain scan could represent their pain and suffering, 80% disagreed that their pain is only real if seen in a brain scan. Conclusions: People with chronic pain recognize the potential benefits of brain imaging but are concerned about data security and dismissal of their self-reported pain. Our data align with previous recommendations to use brain imaging as an adjunct to pain self-reports but not as a replacement for the same.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".