Addressing the Current Knowledge and Gaps in Research SurroundingLysergic Acid Diethylamide (LSD), Psilocybin, and Psilocin in RodentModels
Bibliographic record
Abstract
Lysergic acid Diethylamide (LSD), psilocybin, and psilocin are being intensively evaluated as potential therapeutics to treat depression, anxiety, substance use disorder, and a host of other psychiatric illnesses. Pre-clinical investigation of these compounds in rodent models forms a key component of their drug development process. In this review, we will summarize the evidence gathered to date surrounding LSD, psilocybin, and psilocin in rodent models of the psychedelic experience, behavioural organization, substance use, alcohol consumption, drug discrimination, anxiety, depression-like behaviour, stress response, and pharmacokinetics. In reviewing these topics, we identify three knowledge gaps as areas of future inquiry: sex differences, oral dosing rather than injection, and chronic dosing regimens. A comprehensive understanding of LSD, psilocybin, and psilocin's in vivo pharmacology may not only lead to their successful clinical implementation but optimize the use of these compounds as controls or references in the development of novel psychedelic therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".