Opioid withdrawal management in the fentanyl era
Bibliographic record
Abstract
We read with interest Torres-Lockhart et al.’s recent review [1] of clinical management of opioid withdrawal. The authors used a patient-centered approach to provide a valuable and comprehensive synthesis of existing evidence. We would like to highlight one major limitation of this evidence: none of the cited studies address withdrawal from fentanyl, fentanyl analogs or other high-potency synthetic opioids, the very substances that are driving catastrophically high overdose rates in North America. Fentanyl is 50–100 times as potent as morphine and approximately 25 times as potent as diacetylmorphine (heroin) [2]. Some fentanyl analogs such as carfentanyl are even more potent by orders of magnitude [3]. In general, higher potency opioids trigger higher rates of mu-opioid receptor desensitization and cyclic adenosine monophosphate (cAMP) up-regulation, leading to more severe withdrawal [4, 5]. Thus, approaches for treating opioid withdrawal from lower potency opioids might not relieve withdrawal symptoms for patients using fentanyl. In communities where the illicit opioid supply is dominated by fentanyl, emerging evidence shows that an approach relying upon non-opioid adjuvants, buprenorphine, 20–40 mg of methadone or 30–60 mg of slow-release oral morphine is often insufficient [6-8]. While opioid withdrawal is typically not fatal, fatalities from opioid withdrawal have been reported due to electrolyte derangements from patients with diarrhea and vomiting who were neglected in carceral settings [9]. There are also catastrophic consequences from delaying or disengaging from care because of under-treated opioid withdrawal. One in six individuals in the United States hospitalized with injection drug use-associated infective endocarditis leaves the hospital as a patient-directed (against medical advice) discharge and patients repeatedly cite undertreated withdrawal as one of the primary reasons for leaving prematurely [10, 11]. This has been associated with higher subsequent mortality [12]. Many other patients with opioid use disorder never make it to hospital in the first place and delay care, in part, because of the expectation of undertreated withdrawal [13]. We desperately need to update evidence on opioid withdrawal management to adapt to the new era of synthetic opioids [14]. However, rather than focusing upon cannabis, herbal supplements such as Kratom, or psychedelics with weak mu-opioid receptor agonism, we instead ask researchers and clinicians to consider medications that are already widely available, approved internationally, and familiar: short-acting opioids [15, 16]. In the United States, short-acting opioids can be administered to hospitalized patients as an adjunct to medical or surgical care, and in Canada they may be used in both in- and outpatient settings [15, 16]. They can be rapidly and safely titrated to therapeutic effect and can facilitate initiation of buprenorphine, methadone, slow-release oral morphine or other opioid agonists for maintenance treatment of opioid use disorder [17-19]. Epidemiologically, there is growing recognition that we have entered a categorically different era of opioid use in North America dominated by synthetic opioids [14]. We owe it to patients to be clear-eyed about the clinical implications of this transition: evidence on withdrawal treatment derived from patients using heroin or short-acting pharmaceutical opioids like oxycodone might not apply to patients using fentanyl. Further research is urgently needed to address the needs of patients experiencing withdrawal from fentanyl and its analogs. A.P.T. and R.A.K. receive support from the Research in Addiction Medicine Scholars (RAMS) program R24DA033211. R.A.K. has received research funding from the Centre for Addiction and Mental Health Discovery Fund and travel awards from the American Psychiatric Association and American Academy of Addiction Psychiatry. None. Ashish Thakrar: Conceptualization. Robert Kleinman: Conceptualization.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".