Bibliographic record
Abstract
OBJECTIVE: To determine whether the hypnosedative drug zopiclone could be an agent for abuse. SOURCES OF INFORMATION: Using MEDLINE and PubMed, English-language medical literature was systematically reviewed for reports of direct drug abuse and addiction. A review was also conducted for clinical trials or patient series that discussed issues of addiction or rebound effects. MAIN MESSAGE: Evidence of drug abuse and dependency was found in case reports and small patient series. Dependency symptoms of severe rebound, severe anxiety, tremor, palpitations, tachycardia, and seizures were observed in some patients after withdrawal. Abuse occurred more commonly among patients with previous drug abuse or psychiatric illnesses. Many clinical trials have found evidence of rebound insomnia after recommended dosages were stopped, albeit for a minority of patients. Comparative studies of zopiclone and benzodiazepines or other "Z" drugs are conflicting. CONCLUSION: Zopiclone has the potential for being an agent of abuse and addiction. While many have suggested that the addictive potential for this and other "Z" drugs is less than for most benzodiazepines, caution should be taken when prescribing this agent for insomnia. Ideally, prescriptions should be given for a short period of time and within the recommended dosage guidelines.
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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".