International Stakeholder Community of Pain Experts and Leaders Call for an Urgent Action on Forced Opioid Tapering
Bibliographic record
Abstract
We, the undersigned, stand as a unified community of stakeholders and key opinion leaders deeply concerned about forced opioid tapering in patients receiving long-term prescription opioid therapy for chronic pain. This is a large-scale humanitarian issue. Our specific concerns involve: Opioid tapering guidelines were created, in part, to decrease harm to patients resulting from high-dose opioid therapy for chronic pain. However, countless “legacy patients” with chronic pain who were progressively escalated to high opioid doses, often over many years, now face additional and very serious risks resulting from rapid tapering or related policies that mandate extreme dose reductions that are aggressive and unrealistic. Rapid forced tapering can destabilize these patients, precipitating severe opioid withdrawal accompanied by worsening pain and profound loss of function. To escape the resultant suffering, some patients may seek relief from illicit (and inherently more dangerous) sources of opioids, whereas others may become acutely suicidal. Regardless of one’s view on the advisability of high-dose opioid therapy, every thoughtful clinician recognizes rapid tapering as a genuine threat to a large number of patients who are often medically complex and vulnerable. Indeed, even slower tapers should include realistic, patient-centered goals that are achievable and account for individual patient factors.
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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 | 0.057 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.027 | 0.047 |
| Insufficient payload (model declined to judge) | 0.045 | 0.010 |
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".