Beyond ecstasy: Alternative entactogens to 3,4-methylenedioxymethamphetamine with potential applications in psychotherapy
Bibliographic record
Abstract
The last two decades have seen a revival of interest in the entactogen 3,4-methylenedioxy-N-methylamphetamine (MDMA) as an adjunct to psychotherapy, particularly for the treatment of post-traumatic stress disorder. While clinical results are highly promising, and MDMA is expected to be approved as a treatment in the near future, it is currently the only compound in its class of action that is being actively investigated as a medicine. This lack of alternatives to MDMA may prove detrimental to patients who do not respond well to the particular mechanism of action of MDMA or whose treatment calls for a modification of MDMA's effects. For instance, patients with existing cardiovascular conditions or with a prolonged history of stimulant drug use may not fit into the current model of MDMA-assisted psychotherapy, and could benefit from alternative drugs. This review examines the existing literature on a host of entactogenic drugs, which may prove to be useful alternatives in the future, paying particularly close attention to any neurotoxic risks, neuropharmacological mechanism of action and entactogenic commonalities with MDMA. The substances examined derive from the 1,3-benzodioxole, cathinone, benzofuran, aminoindane, indole and amphetamine classes. Several compounds from these classes are identified as potential alternatives to MDMA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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, 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".