Sustained Use of Benzodiazepines and Escalation to High Doses in a Canadian Population
Bibliographic record
Abstract
OBJECTIVE: "Antibenzodiazepine" campaigns have been conducted worldwide to limit the prescribing of these drugs because of concerns about inappropriate use and addiction. The causal relationship between long-term use and escalation to high doses has not been proven. This study assessed the extent of dose escalation among individuals who were long-term users of benzodiazepines or Z-hypnotics. METHODS: A population-based study was conducted in the Canadian province of Manitoba using administrative health databases. Sustained use was defined as continuous use for at least two years (N=12,598). Dose escalation, measured in diazepam milligram equivalents (DMEs) per day and observed at six-month intervals, was assessed by using latent-class trajectory analysis. Characteristics of individuals with sustained use were described. RESULTS: The analysis revealed four distinct groups. Two groups (<8% of the cohort) showed escalation to high doses (over 40 DMEs). More than 55% of high-dose escalators were in the 0- to 44-year age group, 75% lived in urban areas, and approximately 75% had a diagnosis of depression. Clonazepam was the drug most commonly involved with dose escalation; among individuals escalating to doses higher than 60 DMEs, 91% were using clonazepam. Rates of "doctor shopping" and "pharmacy hopping" were higher among younger adults, compared with older adults. Younger adults also had higher rates of concomitant antidepressant therapy. CONCLUSIONS: A limited segment of a population that received benzodiazepine prescriptions was classified as sustained users, and a small proportion of that group escalated to doses higher than those recommended by product monographs and clinical guidelines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".