Off-label prescription of benzodiazepines: a retrospective cohort study of prescribing prevalence in primary care.
Bibliographic record
Abstract
Background Benzodiazepines are commonly prescribed medications approved for and used in the treatment of anxiolytic and sleep disorders, as well as for seizures, and alcohol withdrawal. However, benzodiazepines are also controlled substances (schedule IV in Canada) because of their potential for abuse and personal harms, which are especially prevalent among older people. It is therefore important to understand how benzodiazepines are being prescribed, and the prevalence of off-label benzodiazepine prescribing, of which very little is known due to challenges in documenting treatment indication. Methods Data from the MOXXI (Medical Office of the XXIst century) electronic health record system in Quebec Canada was used, where specifying the treatment indication for each prescription is required, to estimate the prevalence of off-label prescribing and indications for off-label use of benzodiazepines. Each drug indication was retrospectively classified as either on-label or off-label according to the Health Canada drug database. Off-label prescriptions were further classified as having class evidence supporting their prescription if another benzodiazepine had been approved for the indication by Health Canada. Results There were 20,125 (17.0%) adult patients prescribed benzodiazepines out of the 118,227 patients enrolled in the MOXXI system. The patients were predominantly female (65.6%), and tended to be older with an average age of 60.14 years (standard deviation = 15.68) at the time of the first benzodiazepine prescription. A total of 101,583 unique prescriptions were written for 14 different benzodiazepines to these patients. An approximately equal number of benzodiazepines were prescribed on and off-label (49.3% on-label, 49.2% off-label), with clonazepam having the highest prevalence of off-label prescription (99.5%). Most off-label prescription indications were classified as having class evidence (95.2%). The most common off-label indication was insomnia; 31.6% of all off-label benzodiazepine prescriptions were for insomnia. Conclusions We found that benzodiazepines were frequently prescribed in the province of Quebec and were prescribed off-label approximately half of the time. When prescribed off-label we found that the majority of these prescriptions were for indications that were approved for at least one benzodiazepine. These findings indicate the importance of reminding physicians on the important differences between benzodiazepines that can substantially impact patient outcomes, particularly in older people.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".