Variations in Prescribing Rates of End-of-Life Medications Among Long-Term Care Residents in Alberta Compared with Ontario—a Retrospective Cohort Study
Bibliographic record
Abstract
Background: Prescribing rates for subcutaneous medications may be an indicator of quality of end-of-life care in long-term care (LTC). It is not known if this system level measure is valid across jurisdictions. We compared prescribing rates of medications used for end-of-life symptom relief among LTC residents in Alberta and Ontario. Methods: This retrospective cohort study of LTC residents compared those who died between January 1, 2017, and March 17, 2020 in Alberta, with a published cohort from Ontario. Prescribed end-of-life medications during a resident's last 14 days of life were extracted from administrative dispensation records. LTC homes were ranked into quintiles based on prescribing rates within each home, and the home characteristics were described. The proportion of residents who transferred out of LTC in the last 14 days of life was also determined, as another quality measure. Results: We identified 10,038 decedents in 117 LTC homes. Among LTC decedents, 16.9% were prescribed ≥1 injectable end-of-life medication and 44.9% were prescribed at least one end-of-life medication by any route of administration, within the last 14 days of life. Across prescribing quintiles, there were no associations with transfer rates prior to death. Comparing Alberta to Ontario, there were markedly lower rates of injectable medicine prescribing (16.9% vs. 64.7%). Potential reasons and data limitations were explored. Conclusions: Rates of injectable end-of life medication prescribing differed across Alberta LTC homes; however, current provincial data limitations impact the validity of using these rates as a comparative indicator of the quality of end-of-life care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".