OP92 Non-Opioid Therapy For Pain Management – Health Technology Assessment In A Time Of Crisis
Bibliographic record
Abstract
Introduction: North America is facing a public health epidemic – the opioid crisis – part of which is attributed to the inappropriate use of opioids in pain management. As such, the 2017 Canadian Guideline for Opioids for Chronic Non-Cancer Pain recommends optimizing non-opioid pharmacotherapy or non-pharmacological therapy to treat chronic pain, before a trial of opioids. However, the Guideline itself is not designed to provide evidence on the effectiveness of these non-opioid alternatives, leaving a gap for those attempting to put the recommendation into practice. Methods: In collaboration with its partners, including clinicians and policymakers, the Canadian Agency for Drugs and Technologies (CADTH) identified the gaps in evidence, and developed an action plan to bridge the evidence gaps to support the optimization of non-opioid alternatives in pain management. Results: Since the release of the Guideline, CADTH produced over 20 Rapid Response reports that synthesize and appraise evidence on non-opioid alternatives in the management of a wide range of pain, both acute and chronic. Additionally, CADTH has also reviewed evidence on multidisciplinary pain treatment programs, and is developing environmental scan reports on the availability and access to non-pharmacological treatments for pain in Canada, and on drugs for emerging non-opioid pain. Further, CADTH developed knowledge mobilization tools based on the evidence reviews. The evidence reviews and tools are used as a resource by CADTH partners, including the Coalition of Safe and Effective Pain Management and McMaster University National Pain Center. Conclusions: This presentation will discuss the role of HTA and CADTH to fill the gaps in evidence for a crucial clinical practice guideline recommendation in a time of public health crisis, and help put the evidence into action. It will present the evidence synthesized by CADTH on various non-opioid alternatives for pain management, while highlighting the remaining gaps in evidence. Understanding the evidence on non-opioid alternatives will inform clinical and policy decisions and potentially reduce inappropriate use of opioids in pain management.
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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 | 0.016 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".