What interventions are effective to taper opioids in patients with chronic pain?
Bibliographic record
Abstract
### What you need to know Opioids are commonly prescribed for short term pain relief in people with chronic pain (not caused by cancer). If they are used long term, most patients develop tolerance, their pain increases, and clinicians gradually escalate the dose (fig 1). Sales of prescribed opioids in the USA quadrupled between 2000 and 2010.1 Media have reported the harms of dependence23 and the increasing number of deaths from accidental overdose.45678910 Other long term harms include immune suppression, hormonal imbalance, falls and fractures, acute myocardial infarction, addiction, sedation, and cognitive impairment.11 Fig 1 Long term use of opioids leads to tolerance and increased levels of pain There is very little guidance on withdrawing or tapering opioids in chronic pain (not caused by cancer). People can fear pain, withdrawal symptoms, a lack of social and healthcare support, and they may also distrust non-opioid methods of pain management.12 People may experience flu like symptoms, irritability, anxiety, nausea, diarrhoea, shivering, yawning, sweating, and altered sleep, generalised aches and pains, and abdominal cramps when they stop opioids.13 It is important to distinguish physical dependence on opioids (the physiological responses and symptoms above) from addiction (a compulsive need to take opioids for pleasure).14 Fewer than one in 30 …
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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 | 0.003 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".