Updates on chronic non-cancer pain management in face of the opioid crisis
Bibliographic record
Abstract
Chronic pain not associated with malignancy is experienced by a significant proportion of the Canadian population. As the quality of life and physical functioning are markedly impaired in patients with chronic non-cancer pain, clinicians have commonly turned to opioid therapy for pain management. Since the 1990s, the steady increase in dispensing of prescription opioids has paralleled trends in opioid-related hospitalizations, overdoses, and fatalities. In fact, over-prescription and longterm opioid therapy are among the many root causes fueling Canada’s rise in opioid addiction and opioid-related deaths. Physicians and medical regulators have responded to this public health crisis by developing the 2017 Canadian Guideline for Opioids for Chronic Non-Cancer Pain. The new evidence-based guideline aims to encourage safe prescribing practices, reduce and eliminate the use of opioid analgesics and promote non-opioid pharmacotherapy. While clear clinical guidelines will optimize physician prescribing patterns, it is imperative to recognize the need for non-pharmacological modalities for pain management, treatment, and care to holistically address the complex roots of opioid abuse.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".