Opioid Use at End-Of-Life Among Nova Scotia Patients With Cancer
Bibliographic record
Abstract
Purpose: To determine the factors associated with opioid analgesic prescriptions as measured by community pharmacy dispensations to all Nova Scotia (NS) patients with cancer at end-of-life from 2005 to 2009. Methods: The NS Cancer Registry and the NS Prescription Monitoring Program (NSPMP) were used to link Nova Scotians who had a cancer diagnosis and received a prescription for opioids in their last year of life ( n = 6,186) from 2005 to 2009. The association of factors with opioid dispensations at end-of-life were determined (e.g., patient demographics, type of prescriber, type of cancer, and opioid type, formulation, and dose). Results: Almost 54% ( n = 6,186) of the end-of-life study population with cancer ( n = 11,498) was linked to the NSPMP and therefore dispensed opioids. Most prescriptions were written by general practitioners (89%) and were for strong opioids (81%). Immediate-release formulations were more common than modified-release formulations. Although the annual average parenteral morphine equivalents (MEQ) did not change during the study period, the number of opioid prescriptions per patient per year increased from 5.9 in 2006 to 7.0 in 2009 ( p < 0.0001). Patients age 80 and over received the fewest prescriptions (mean 3.9/year) and the lowest opioid doses (17.0 MEQ) while patients aged 40–49 received the most prescriptions (mean 14.5/year) and the highest doses of opioid (80.2 MEQ). Conclusion: Our study examined opioid analgesic use at end-of-life in patients with cancer for a large real-world population and determined factors, trends and patterns associated with type and dose of opioid dispensed. We provide information regarding how general practitioners prescribe opioid therapy to patients at end-of-life. Our data suggest that at the time of this study, there may have been under-prescribing of opioids to patients with cancer at end-of-life. This information can be used to increase awareness among general practitioners, and to inform recommendations from professional regulatory bodies, to aid in managing pain for cancer patients at end-of-life. Future work could address how opioid prescribing has changed over time, and whether efforts to reduce opioid prescribing in response to the opioid crisis have affected patients with cancer at end-of-life in Nova Scotia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".