Novel treatment of opioid use disorder using ibogaine and iboga in two adults
Bibliographic record
Abstract
Abstract Ibogaine is a naturally occurring psychedelic medicine with anti-addictive properties. While research on ibogaine is limited, several observational studies have shown ibogaine can mitigate opioid withdrawal, as seen with reductions in clinical and subjective opioid withdrawal scores and reduced drug use severity (Noller, Frampton, & Yazar-Klosinski, 2018; Brown & Alper 2018). Furthermore, the psychoactive experience may help individuals to realign their values, purpose and sense of connection, as seen with post treatment reductions in depression scores (Noller et al., 2018; Mash et al., 2000). Case series This case series describes two cases of individuals accessing ibogaine through private unregulated clinics in the Vancouver area to treat their opioid use disorder. Conclusions In case 1, the client achieved total abstinence from all opioids within 5–6 days of starting ibogaine treatment, did not experience any opioid withdrawal symptoms after ibogaine treatment and maintained abstinence from opioids for 3 years. In case 2, the patient took ibogaine/iboga in multiple treatments over a short period of time (<4 months). The patient stopped all non-medical opioids after the first iboga treatment and then used ibogaine to aid with further dose reductions of her opioid agonist therapy (OAT) and has maintained abstinence from opioids for 2 years. Ibogaine offers a unique and novel therapeutic approach to treating opioid use disorder. Further studies are needed to establish the safety, risks and potential role for ibogaine as a mainstream, evidence-based addiction 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".