Mediating Effect of Pain Sensitization on the Paradoxical Relation of Taking Opioids to Pain Severity in Knee Osteoarthritis: The Multicenter Osteoarthritis Study
Bibliographic record
Abstract
OBJECTIVE: One of the less understood adverse effects while taking opioids is the paradoxical increase in pain, known as opioid-induced hyperalgesia (OIH). We sought to determine whether pain sensitization mediates the relation of taking an opioid to pain severity in people with knee osteoarthritis (OA). METHODS: We included participants in a National Institutes of Health-funded cohort study of people with or at risk of knee OA. Participants were categorized into opioid and nonopioid analgesic groups at baseline. Western Ontario McMaster Universities OA Index (WOMAC) pain two years later was assessed as the outcome. We used causal mediation analysis to assess the mediating role of pain sensitization, quantified by changes in pressure pain threshold (PPT) at the wrist and patella over two years, on the effect of taking an opioid on WOMAC pain two years later. RESULTS: We included 296 participants who took opioids and 1,070 participants who took nonopioid analgesics. Compared with taking nonopioid analgesics, taking opioids was associated with greater pain two years later. This relation was mediated by 0.05- and 0.08-unit changes in wrist PPT (95% confidence interval [CI] 0.01-0.10) and patellar PPT (95% CI 0.02-0.14), respectively. When we assessed any worsening in WOMAC pain score over two years, taking opioids, compared with taking nonopioid analgesics, had 2% and 5% higher odds of experiencing any worsening pain mediated by changes in wrist PPT (95% CI 0.99-1.04) and patellar PPT (95% CI 1.01-1.09), respectively. CONCLUSION: Pain sensitization had small mediating effects on the paradoxical phenomenon of OIH, suggesting that pain sensitization may not play a major role and/or that PPT is an inadequate tool to assess OIH.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".