Bibliographic record
Abstract
Liebrenz et al. [1] suggested that agonist substitution could be an alternative for some benzodiazepine-dependent patients. In their thoughtful review, they compared the current usual treatment of benzodiazepine-dependent patients with the approach used in opiate dependence since the early 1960s, for which agonist therapy is considered as the gold standard. The use of nicotine replacement in nicotine-dependent smokers also supports the hypothesis that agonist treatment can be useful in the management of substance users other than opiates. The efficacy of smoking cessation medication in supporting abstinence appears to be related directly to its ability to suppress withdrawal symptoms [2]. Furthermore, some evidence suggests that delta-9-tetrahydrocannabinol (THC), the main psychoactive component of cannabis, decreased withdrawal symptoms in cannabis-dependent patients [3–5], whereas various non-agonist treatments showed a lack of efficacy (for a recent review, see [6]). Nicotine replacement treatment and methadone do not usually give users a great deal of positive reinforcement for continued use, but rather prevent the negative reinforcement of withdrawal. Similarly, THC is not highly reinforcing in itself, even in chronic users [7], which might be a factor in successful agonist treatment that leads eventually to abstinence. In any case, the use of an oral form of THC might be expected to avoid the pulmonary complications of smoking cannabis [8]. While these results remain preliminary, they suggest that the effectiveness of agonist treatments is well known in both opiate and nicotine dependences and warrant further research, not only in benzodiazepine dependence, as suggested by Liebrenz et al., but also in cannabis dependence. The author is funded by a grant from the Société francaise de tabacologie (SFT) and the Addiction Program of CAMH.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".