Inpatient Z-drug use commonly exceeds safe dosing recommendations
Bibliographic record
Abstract
IMPORTANCE: In 2016 recommendations for safer prescribing practices were circulated to all doctors in one of Canada's largest provinces, by the college of physicians, following a coroner's inquest into a vehicular death related to Z-drug use. We sought to determine how frequently Z-drug prescriptions in our institution were not adhering to these recommendations. DESIGN: Retrospective cohort study. SETTING: McGill University Health Centre, an 832-bed tertiary care institution in Montréal, Canada. PARTICIPANTS: All adult non-obstetrical patients admitted between April 1, 2015 and March 31, 2016. EXPOSURE: The receipt of at least one dose of Z-drug as determined by pharmacy records. MAIN OUTCOMES AND MEASURES: Adherence to four recommendations related to starting dose, maximal dose, concomitant drug administration, and duration of use were evaluated. RESULTS: 1,409 unique patients received a Z-drug during 1,783 admissions representing use in 9.3% of non-obstetrical patients. Standing orders were seen in 42% (745/1783) of admissions. Non-conformity with the coroner's recommendations was common. Overall, 672/1783 (38%) admissions involved a patient receiving more than the recommended daily maximum dose (643/999 older patients, 64%). Of 607 admissions which were longer than 10 days, 257 (39%) involved a prescription which exceeded 10 days. CONCLUSIONS AND RELEVANCE: A coroner's recommendation that doctors receive instructions about safe Z-drug prescribing is unprecedented, and was likely required given that use of Z-drugs occurs at doses and durations that often exceed best practice recommendations. Similar interventions may be required in other jurisdictions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".