Pharmacist-led sedative-hypnotic deprescribing in team-based primary care practice
Bibliographic record
Abstract
Background: Sedative-hypnotic (SH) medications are often used to treat chronic insomnia, with potentially serious long-term side effects. The objective of this study is to evaluate an interprofessional SH deprescribing program within a community team-based, primary care practice, with or without cognitive behavioural therapy for insomnia (CBT-I). Methods: Retrospective chart review for patients referred to the team pharmacist for SH deprescribing from February 2016 to June 2019. Results: A total of 121 patients were referred for SH deprescribing, with 111 (92%) patients who attempted deprescribing (average age 69, range 29-97 years) and 22 patients who also received CBT-I. Overall, 36 patients (32%) achieved complete abstinence, and another 36 patients (32%) reduced their dosage by ≥50%. For the 36 patients who achieved complete abstinence, 26 (72%) patients remained abstinent at 6 months (9 patients resumed using SH and 1 patient was lost to follow-up). The proportion of patients achieving complete abstinence or reduced dosage of ≥50% (successful tapering) was higher with CBT-I than without CBT-I but did not reach statistical significance (77% vs 62%, p = 0.22). There were also no statistically significant differences detected in the success between those who took a benzodiazepine and those who took a Z-drug (67% vs 61%, p = 0.55) or for those who took SH daily and those who took them intermittently (67% vs 44%, p = 0.09). Conclusion: Almost two-thirds of patients participating in our pharmacist-led program were able to stop or taper their SH medications by ≥50%. The role of CBT-I in SH deprescribing remains to be further elucidated. Can Pharm J (Ott) 2021;154:xx-xx.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".