Time trends in opioid use in cancer and noncancer patients: Observations from administrative data.
Bibliographic record
Abstract
298 Background: Opioid prescribing has been increasingly scrutinized in the non-cancer patient population due to concerns with morbidity, mortality and diversion. Resulting regulatory changes have decreased prescribing. As an unintended consequence, we hypothesized that cancer patients might be similarly impacted. Methods: All Ontario residents ≥ 65 years are eligible for government paid pharmacare. For each year from 2004 to 2013, Ontarians ≥ 65 years were stratified into 3 groups: no cancer history, cancer diagnosis > 5 years ago and cancer diagnosis ≤ 5 years ago. We evaluated time trends in 2 outcomes: (1) opioid prescription rate = total number of pharmacare claims / total population of ≥ 65 year olds, and (2) mean daily opioid dose (in morphine equivalents) = sum of all patient’s mean daily opioid doses over their first 90 days of opioid therapy in each year / total patients with an opioid prescription in that year. Results: Ontario’s population of ≥ 65 year olds increased each year with 2.1 million residents in 2013 (5% recent cancer, 10% remote cancer). Demographics were similar by year for each group. Across all years, overall opioid prescription rates were highest for those with a recent cancer and lowest for those with no cancer history. Overall prescription rates increased over time by 23%, 14% and 10% in the non-cancer, remote cancer and recent cancer groups respectively, primarily due to increases in long acting opioids and immediate release single agents. Decreases were seen in long acting oxycodone and fentanyl prescriptions in all cancer groups. The mean daily opioid dose increased for patients receiving long acting oxycodone and fentanyl but was relatively stable for other drug classes. With the exception of immediate release single agents, the mean daily dose was similar between cancer groups regardless of drug class. Conclusions: Regulatory measures have succeeded in decreasing prescription rates in some but not all drug classes among Ontarians aged ≥ 65. Changes over time in both prescription rates and mean daily opioid dose were similar for all 3 groups, suggesting that factors influencing prescribing are affecting cancer and non-cancer patients similarly, possibly to the detriment of some cancer patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| 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) | 0.002 | 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".