Long-term effects of benzodiazepine discontinuation among older adults: potential improvements on depressive symptoms
Bibliographic record
Abstract
OBJECTIVES: To examine how change in benzodiazepine (BZD) use is linked to changes in depressive symptoms intensity, worry intensity, and sleep quality over 16 months. METHOD: Data come from a larger randomised controlled trial (RCT) named the 'Programme d'Aide du Succès au SEvrage (PASSE-60+)' study (NCT02281175). Seventy-three participants age 60 years and older took part in a 4-month discontinuation programme and were assessed four times over 16 months. Change in BZD use was defined as the difference in reported mg/day between two assessments. Control variables were RCT discontinuation group; BZD use at T1; and either depressive symptoms, worry intensity, or sleep quality at T1. Hierarchical multiple regressions were used to analyse data. RESULTS: In the short term, right after the discontinuation programme, sleep quality worsened with lower BZD use. This link was no longer significant at the 3- and 12-month follow-up. In the long term, depressive symptoms lowered with lower BZD use. No change was found in worry intensity in relation to BZD use at all measurement times. CONCLUSION: Discontinuation may improve depressive symptoms. Our study also questions the long-term effectiveness of BZD use, since long-term discontinuation was not linked with change in worry intensity and sleep quality.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".