Bibliographic record
Abstract
This "Designer Drugs 2.0" issue of Clinical Pharmacology & Therapeutics focuses on novel psychoactive substances, primarily cannabinoids and cathinones, and the repurposing of established psychoactive compounds (e.g., modafinil, psilocybin, lysergic acid diethylamide, and 3,4-methylenedioxymethamphetamine) that simultaneously offer new pharmacotherapies and pose serious health problems. Novel psychoactive substances were initially used as potent tools to investigate endogenous neurotransmitter systems; for example, synthetic cannabinoids have much higher potency than Δ9-tetrahydrocannabinol at the cannabinoid receptors. However, they are now being used illicitly as well as being tested for their efficacy in numerous clinical indications. Likewise, previously established psychoactive drugs are being repurposed as treatments for a wide variety of indications where currently approved medications are ineffective. This set of papers examines the arising problems associated with designer drugs (e.g., adverse events, psychosis, rapid new synthesis, abuse liability testing, internet sales, scheduling) as well as the potential therapeutic promises in areas as diverse as cognition enhancement, exercise-mimetics, epilepsy, multiple sclerosis, and posttraumatic stress disorder.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.086 | 0.087 |
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".