Single treatments that have lasting effects: some thoughts on the antidepressant effects of ketamine and botulinum toxin and the anxiolytic effect of psilocybin
Bibliographic record
Abstract
Recent clinical trials suggest that 3 single biological treatments have effects that persist. Based on research showing that the muscles involved in facial expressions can feed back to influence mood, a single trial diminishing glabella frown lines with botulinum toxin demonstrated a significant antidepressant effect for 16 weeks. Based primarily on research with animal models of depression suggesting that glutamate may be involved in depression, the N-methyl-D-aspartate antagonist ketamine has been tested in several trials. A single dose decreased depression for up to a week. The reported effects of the use of mushrooms containing psilocybin by a number of cultures around the world has stimulated several trials showing beneficial effects of a single dose of psilocybin for over a year in healthy people, and for up to 3 months in patients with anxiety disorders who have advanced cancer. This article discusses these studies, their rationale, their possible mechanisms of action, the future clinical research required to establish these therapies and the basic research required to optimize single treatments that have lasting effects.
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
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".