Predicting chronic benzodiazepine use in adults with depressive disorder
Bibliographic record
Abstract
Objective To identify predictive variables of incident chronic benzodiazepine (BZD) use that could be assessed by prescribing physicians. Design Retrospective cohort study using public health and drug insurance administrative data. Setting Quebec. Participants New adult BZD users from January 1, 1999, to March 31, 2006, with a diagnosis of depressive disorder in the previous year were included. Chronic BZD use was defined as BZD availability at least 50% of the days between day 181 and day 365 following initiation. Main outcome measures Potential associations between chronic BZD use and age; sex; drug insurance status; recent hospitalization; comorbidity; presence of chronic pain; use of psychotropic medication; mental health diagnoses; number, type, and duration of BZDs prescribed; and the prescribing physician’s specialty. Results Selection led to an exhaustive cohort of 13 688 patients aged 18 to 64 years, and 3683 aged 65 and older. For the 18 to 64 age group, the combination of disability insurance and more than 1 BZD increased the proportion of chronic users from 14.4% to 53.4%. For patients 65 and older, the main correlates of chronic BZD use included claiming more than 1 BZD (adjusted odds ratio 2.24, 99% CI 1.65 to 3.06) and recent hospitalization (adjusted odds ratio 1.70, 99% CI 1.38 to 2.10). Recently hospitalized older patients with a prescription duration of less than 8 days were the highest-risk group identified (57.8%). Conclusion Physicians should be aware that patients are more likely to become chronic BZD users if they receive disability insurance or following a hospitalization. Combination of BZDs is a potentially problematic practice that could be increasing the risk of chronic use.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".