Attitudes Towards and Management of Opioid-induced Hyperalgesia
Bibliographic record
Abstract
OBJECTIVES: Opioid-induced hyperalgesia (OIH) is a phenomenon whereby opioids increase patients' pain sensitivity, complicating their use in analgesia. We explored practitioners' attitudes towards, and knowledge concerning diagnosis, risk factors, and treatment of OIH. MATERIALS AND METHODS: We administered an 18-item cross-sectional survey to 850 clinicians that managed chronic pain with opioid therapy. RESULTS: The survey response rate was 37% (318/850). Most respondents (240/318, 76%) reported they had observed patients with OIH in their practice, of which 38% (84/222) reported OIH affected >5% of their chronic pain patients. The majority (133/222, 60%) indicated that OIH could result from any dose of opioid therapy. The most commonly endorsed chronic pain conditions associated with the development of OIH were fibromyalgia (109/216, 51%) and low back pain (91/216, 42%), while 42% (91/216) indicated that no individual chronic pain condition was associated with greater risk of OIH. The most commonly endorsed opioids associated with the development of OIH were oxycodone (94/216, 44%), fentanyl (86/216, 40%), and morphine (84/216, 39%); 27% (59/216) endorsed that no specific opioid was more likely to result in OIH. Respondents commonly managed OIH by opioid dose reduction (147/216, 68%), administering a nonopioid adjuvant (133/216, 62%), or discontinuing opioids (95/216, 44%). DISCUSSION: Most clinicians agreed that OIH is a complication of opioid therapy, but were divided regarding the prevalence of OIH, etiological factors, and optimal 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".