Withdrawal-associated injury site pain (WISP): a descriptive case series of an opioid cessation phenomenon
Bibliographic record
Abstract
Withdrawal pain can be a barrier to opioid cessation. Yet, little is known about old injury site pain in this context. We conducted an exploratory mixed-methods descriptive case series using a web-based survey and in-person interviews with adults recruited from pain and addiction treatment and research settings. We included individuals who self-reported a past significant injury that was healed and pain-free before the initiation of opioids, which then became temporarily painful upon opioid cessation-a phenomenon we have named withdrawal-associated injury site pain (WISP). Screening identified WISP in 47 people, of whom 34 (72%) completed the descriptive survey, including 21 who completed qualitative interviews. Recalled pain severity scores for WISP were typically high (median: 8/10; interquartile range [IQR]: 2), emotionally and physically aversive, and took approximately 2 weeks to resolve (median: 14; IQR: 24 days). Withdrawal-associated injury site pain intensity was typically slightly less than participants' original injury pain (median: 10/10; IQR: 3), and more painful than other generalized withdrawal symptoms which also lasted approximately 2 weeks (median: 13; IQR: 25 days). Fifteen surveyed participants (44%) reported returning to opioid use because of WISP in the past. Participants developed theories about the etiology of WISP, including that the pain is the brain's way of communicating a desire for opioids. This research represents the first known documentation that previously healed, and pain-free injury sites can temporarily become painful again during opioid withdrawal, an experience which may be a barrier to opioid cessation, and a contributor to opioid reinitiation.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".