Concordance with a STOPP (Screening Tool of Older Persons' Potentially Inappropriate Prescriptions) Criterion in Nova Scotia, Canada: Benzodiazepine and Zoplicone Prescription Claims by Older Adults with Fall-related Hospitalizaions.
Bibliographic record
Abstract
BACKGROUND: Optimization of prescribing in older adults is needed. The STOPP criteria provide a systematic way of identifying potentially inappropriate prescribing in this population. Previous research indicates poor concordance between benzodiazepine prescribing and STOPP. OBJECTIVES: To determine the extent and predictors of benzodiazepine and zopiclone (BZD-Z) pharmacy dispensations in older adults with a history of a recent fall, in concordance with STOPP. METHODS: Prescription claims data from the Nova Scotia Seniors' Phamacare Program were linked with fall-related injury data from the CIHI Discharge Abstract Database. Adults aged ≥ 66 years making a claim for a BZD-Z in the 100 days prior to fall-related hospitalization were identified. Their BZD-Z claims in the 100 days following discharge were also identified. Descriptive statistics, trend tests and logistical regression modelling were performed to examine predictors for continued use of BZD-Z post-fall. RESULTS: Over 5 years, from a pool of 8,271 older adults discharged following a fall-related hospitalization, 1,789 (21.6%) had made a claim for a BZD-Z in the 100 days prior to admission. Of these, 82% were women. Younger age and female sex were predictors of continuing BZD-Z dispensations post-fall. In the 100 days following discharge, 74.2% (n=1327) made a claim for at least one BZD-Z. CONCLUSION: BZD-Z use continued in 74% of patients following discharge from a fall-related hospitalization, representing limited concordance with the STOPP criterion. Such hospitalizations and follow-up care present an opportunity to address an ongoing modifiable risk factor.
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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 | 0.007 |
| 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 | 0.000 | 0.001 |
| 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".