Which is challenging: Chronic Pain or Chronic Pain-associated Medical Education/Training?
Bibliographic record
Abstract
Chronic pain is perceived by physicians and healthcare systems worldwide as a major challenge, costing US $650 billion per year, which is more than the costs of cancer, cardiovascular diseases, and diabetes [1]. Despite major efforts to find cost-effective solutions, these efforts are heading in the wrong direction. Worldwide, chronic pain-associated knowledge and pain practices are dissociated, and approaches to diagnosis and treatment are mostly based on outdated knowledge and are highly reductionist. Research, medical education, legislation priorities, and directions are influenced by economic dominance, and chronic pain clinical practices, for a significant majority, are going against medical ethics, evidencebased medicine, and cost-effectiveness. In USA, chronic pain patients are misdiagnosed 40-80% of times according to research from John Hopkins Hospital physicians [2]. Over the past 30 years to date, a huge body of research evidence from the perspectives of conventional pain medicine, complementary/integrative pain medicine, and regenerative pain medicine has not been incorporated into chronic pain medical education/training. Therefore, an extensive and comprehensive 30-month clinical fellowship training program was created at McMaster University in Canada (2007–2010) to fill these gaps. Its main outcome is a major shift in pain management goals from extremely costly, unsafe pain relief to the cost-effective treatment or curing of most chronic pain syndromes and their underlying causes.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".