Identifying appropriate outcomes to help evaluate the impact of the Canadian Guideline for Safe and Effective Use of Opioids for Non-Cancer Pain
Bibliographic record
Abstract
BACKGROUND: The Canadian Guideline for Safe and Effective Use of Opioids for Chronic Non-Cancer Pain (COG) was developed in response to increasing rates of opioid-related hospital visits and deaths in Canada, and uncertain benefits of opioids for chronic non-cancer pain (CNCP). Following publication, we developed a list of evaluable outcomes to assess the impact of this guideline on practice and patient outcomes. METHODS: A working group at the National Pain Centre at McMaster University used a modified Delphi process to construct a list of clinical and patient outcomes important in assessing the uptake and application of the COG. An advisory group then reviewed this list to determine the relevance and feasibility of each outcome, and identified potential data sources. This feedback was reviewed by the National Faculty for the Guideline, and a National Advisory Group that included the creators of the COG, resulting in the final list of 5 priority outcomes. RESULTS: Five outcomes were judged clinically important and feasible to measure: 1) Effects of opioids for CNCP on quality of life, 2) Assessment of patient's risk of addiction before starting opioid therapy, 3) Monitoring patients on opioid therapy for aberrant drug-related behaviour, 4) Mortality rates associated with prescription opioid overdose and 5) Use of treatment agreements with patients before initiating opioid therapy for CNCP. Data sources for these outcomes included patient's medical charts, e-Opioid Manager, prescription monitoring programs and administrative databases. CONCLUSION: Measuring the impact of best practice guidelines is infrequently done. Future research should consider capturing the five outcomes identified in this study to evaluate the impact of the COG in promoting evidence-based use of opioids for CNCP.
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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 | 0.114 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".