The intersection of harm reduction and postoperative care for an illicit fentanyl consumer after major surgery: A case report
Bibliographic record
Abstract
Background As Canada continues to address challenges related to the opioid crisis, individuals suffering from opioid use disorder (OUD) can be especially vulnerable to physical and psychological destabilization after surgery. Adopting a harm reduction approach postoperatively can be a success factor for safe recovery and satisfactory analgesia.Purpose We present the case of a 40-year-old patient (referred to as DC) with OUD using illicit fentanyl, heroin, and oxycodone preoperatively and admitted for an elective liver resection for steroid-induced hepatoma. Despite a preoperative anesthesia assessment and the initiation of a standard balanced multimodal analgesic regimen, suboptimal analgesia was evident in the first 24 h postoperatively. This lack of analgesic efficacy precipitated DC’s use of illicit self-injected intravenous (IV) opioid and significant emotional distress. To address this, a nurse practitioner and anesthesiologist within the Toronto General Hospital acute and transitional pain program and the surgical team quickly met and adopted a harm reduction approach to manage DC’s postoperative pain and emotional distress. The ultimate goal was to eliminate self-administration of illicit IV opioids and prevent DC from attempting to leave hospital against medical advice. Following an interprofessional team discussion that included DC, IV fentanyl was offered via a patient-controlled analgesia pump to DC’s satisfaction (exceeding standard settings), providing acceptable pain relief. To our knowledge, DC did not self-administer additional illicit drugs during the remainder of hospitalization.Outcome This harm reduction approach resulted in DC’s safe recovery, achievement of postoperative functional milestones, and continued engagement with outpatient pain treatment.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| 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".