Combined Morphine and Methadone Treatment: Two Case Reports
Bibliographic record
Abstract
Background: The vast majority of accidental opioid toxicity deaths in Canada were fentanyl-related in 2022. The strongest evidence-based treatment defense against such deaths is opioid agonist treatment. In the hope of increasing treatment retention in opioid agonist treatment, recent guidelines have endorsed the addition of slow-release oral morphine (SROM) to methadone maintenance treatment (MMT) as a long-term option and anecdotally, the practice is starting to take hold in some medical practices. Case Reports: Two case reports of patients who were prescribed both SROM and MMT concurrently for ongoing maintenance are presented and examined here. Discussion: These case reports demonstrate the possible benefit of combining SROM with MMT for long-term opioid agonist maintenance. Clinician and patient self-report narratives included an improved sense of well-being, stability, and reduced fentanyl and other opioid use. Contexte: La grande majorité des décès accidentels dus à l’intoxication aux opioïdes au Canada étaient liés au fentanyl en 2022. Le traitement par agoniste opioïde est le meilleur moyen de défense fondé sur des données probantes contre ces décès. Dans l’espoir d’accroître la rétention du traitement par agoniste opioïde (TAO), des lignes directrices récentes ont approuvé l’ajout de morphine orale à libération lente (MOLL) au traitement d’entretien à la méthadone (TEM) comme option à long terme et, de façon non officielle, la pratique commence à s’implanter dans certains cabinets médicaux. Rapports de cas: Deux rapports de cas de patients à qui l’on a prescrit simultanément de la MOLL et le TEM pour un traitement d’entretien continu sont présentés et examinés ici. Discussion: Ces rapports de cas démontrent les avantages possibles de la combinaison de la MOLL et du TEM pour l’entretien à long terme des agonistes opioïdes. Les récits des cliniciens et des patients comprennent une amélioration du sentiment de bien-être et de stabilité, ainsi qu’une réduction de la consommation de fentanyl et d’autres opioïdes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".