Three Decades of Research on the Development of Ibogaine Treatment of Substance Use Disorders: A Scientometric Analysis
Bibliographic record
Abstract
Ibogaine is a natural psychoactive drug that has been investigated for its potential role in the treatment of substance use disorders since the mid-1960s. To evaluate the interest in ibogaine's use as a therapeutic agent, we performed a scientometric analysis covering the last three decades (1993-2002, 2003-2012, and 2013-2022). A complementary analysis was performed to select and describe published clinical trials and meta-analyses. A total of 1523 references were found. Linear growth of publications in the first and third decades were identified, and the average number of publications from 1993 to 2002 was lower than that in the other two decades. Researchers from five continents were identified. Globally, academic research centers in the United States and Canada were the most productive. Cocaine, tobacco, morphine, and alcohol prevailed as major keywords in the first two decades and opioids and psychedelics were included in the third decade. A few key authors were the most co-referenced. One preclinical meta-analysis and no meta-analysis in humans were found. Research trends for ibogaine are widespread, growing, and consonant with current attentiveness in drug abuse. Our findings support the pressing need for rigorous clinical research on ibogaine to evaluate its efficacy and safety.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.032 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.096 | 0.126 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".