The role of prescribing practices in managing chronic pain with opioids
Bibliographic record
Abstract
The role of prescribing practices in managing chronic pain with opioids Norm Buckley and Jason Busse from the Michael G. DeGroote Institute for Pain Research and Care discuss prescribing practices, managing chronic pain with opioids, and the contribution of licit and illicit opioids towards the Canadian opioid crisis. Over the past 40 years, chronic pain treatment has ranged from pharmacotherapy, regional anesthesia techniques, graduated exercise, physiotherapy modalities, lifestyle modification, psychotherapy (e.g., cognitive behavioral therapy), and complementary therapies such as acupuncture, yoga, and Tai Chi. Pharmacotherapy included the use of anticonvulsant drugs such as carbamazepine with their risks of liver dysfunction, non-steroidal anti-inflammatories with the risk of bleeding and renal injury, anti-depressants with serotonin, cholinergic and adrenergic actions, and complications including sedation and cardiac arrhythmia, and opioids.
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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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".