Linking opioid-induced hyperalgesia and withdrawal-associated injury site pain: a case report
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Understanding the details of one individual's experience with pain, opioid use and withdrawal may generate insights into possible relationships between opioid-induced hyperalgesia and withdrawal-associated injury site pain (WISP). METHODS: This case study was extracted from a mixed methods study that characterized WISP. In 2014, the individual was recruited from a primary care clinic that prescribes opioid agonist therapy. In an interview, she completed a 35-item survey and elaborated on her own experience. Follow-up contact was made in June of 2017. RESULTS: This 34-year-old white woman had several twisting injuries of her right knee between ages 13 and 15. The pain resolved each time in a few days, and she was pain free for 15 years. Around age 30, she initiated illicit oxycodone recreationally (not for pain) and developed an opioid use disorder. On detoxification, she experienced severe knee pain for 6 weeks that resolved postdetoxification but returned after subsequent oxycodone use and withdrawal episodes along with generalized skin sensitivity. This experience of WISP became a barrier to opioid cessation. Although nonsteroidal anti-inflammatories and gabapentin relieved WISP and methadone therapy assisted her opioid use disorder, an eventual change to sublingual buprenorphine/naloxone provided superior control of both. CONCLUSION: This case report illustrates that both opioid use and withdrawal can reactivate injury site pain, which can increase with dose escalation and repeated withdrawal events. The timing, trajectory, and neuropathic features of WISP reported here are consistent with those previously reported for the development of opioid-induced hyperalgesia, possibly linking these phenomena.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".