Analgesic potential of macrodoses and microdoses of classical psychedelics in chronic pain sufferers: a population survey
Bibliographic record
Abstract
Although several studies and reports have shown the potential analgesic use of serotonergic psychedelics in cancer pain, phantom limb pain and cluster headache, evidence supporting their use for chronic pain is still limited. The past years have seen a considerable renewal of interest toward the therapeutic use of these compounds for mood disorders, resulting in a marked increase in the number of people turning to psychedelics in an attempt to self-medicate a health condition or improve their wellbeing. In western countries particularly, this population of users overlaps substantially with chronic pain sufferers, representing a unique opportunity to evaluate the effects these compounds have on pain and wellbeing. Here, we report results from an online survey conducted between August 2020 and July 2021 in a population of 250 chronic pain sufferers who had experience with psychedelics, either in microdoses (small sub-hallucinogenic doses), macrodoses (hallucinogenic doses), or both. Macrodoses, while less often used for analgesic purposes than microdoses, were reported to induce a higher level of pain relief than both microdoses and conventional pain medications (including opioids and cannabis). Although the effects were weaker and potentially more prone to expectation bias than with macrodoses, our results also suggested some benefits of psychedelics in microdoses for pain management. The reported analgesic effect appeared unrelated to mood improvements associated with psychedelic use, or the advocacy of psychedelic use. Taken together, our findings indicate interesting potential analgesic applications for psychedelics that warrant further clinical research.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".