Canadian guideline for safe and effective use of opioids for chronic noncancer pain: clinical summary for family physicians. Part 1: general population.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To provide family physicians with a practical clinical summary of the Canadian Guideline for Safe and Effective Use of Opioids for Chronic Non-Cancer Pain, developed by the National Opioid Use Guideline Group. QUALITY OF EVIDENCE: Researchers for the guideline conducted a systematic review of the literature on the effectiveness and safety of opioids for chronic noncancer pain, and drafted a series of recommendations. A panel of 49 clinicians from across Canada reviewed the draft and achieved consensus on 24 recommendations. MAIN MESSAGE: Screening for addiction risk is recommended before prescribing opioids. Weak opioids (codeine and tramadol) are recommended for mild to moderate pain that has not responded to first-line treatments. Oxycodone, hydromorphone, and morphine can be tried in patients who have not responded to weaker opioids. A low initial dose and slow upward titration is recommended, with patient education and close monitoring. Physicians should watch for the development of complications such as sleep apnea. The optimal dose is one which improves function or decreases pain ratings by at least 30%. For by far most patients, the optimal dose will be well below a 200-mg morphine equivalent dose per day. Tapering is recommended for patients who have not responded to an adequate opioid trial. CONCLUSION: Opioids play an important role in the management of chronic noncancer pain, but careful prescribing is needed to limit potential harms. The new Canadian guideline provides much-needed guidance to help physicians achieve a balance between optimal pain control and safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it