Is there an association between the range of benzodiazepines available and the quality of prescribing?: An international comparison of Canada and Australia using administrative data
Bibliographic record
Abstract
Background: Drug utilization 90% (DU90%) represents the number and volume of compounds that account for 90% of total Defined Daily Doses (DDDs)/1000 patients/day. DU90% may be an indicator of quality in drug prescribing on the basis that prescribers have a better knowledge of the available treatments including their benefits, risks and dosages when they have to be familiar with less medications within a drug class.Methods: We compared the association between DU90% and overall rates of benzodiazepine prescribed between Nova Scotia (NS) and Australia in seniors, as benzodiazepine side‐effects are greatest in the over‐ 65s. We used the NS Pharmacare Programme and Australian Pharmaceutical Benefits Scheme to obtain dispensing data for all publicly‐subsidized benzodiazepines and relatedcompounds from 2000‐3. We used the WHO Anatomic Therapeutic Chemical/ Defined Daily Dose (DDD) system.Results: Benzodiazepine prescription in Nova Scotia was more than double that of Australia from 2000 (123 and 48 DDD/1000 beneficiaries per day) through 2003 (138 and 57 DDD/1000 beneficiaries per day). 17 types of benzodiazepines were used in Nova Scotia in 2003 compared to five in Australia. In terms of DU90%, 8 benzodiazepines made up 90% of the use in Nova Scotia. By contrast, only fourdifferent benzodiazepines made up 90% of the use in Australia.Conclusions: There is an association between the total level of benzodiazepine prescribing, the number of different benzodiazepines available and less appropriate use in seniors, as measured by an indicator of quality prescribing. Limiting the range of available benzodiazepines may promote more appropriate prescription.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 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".