Cancer survivorship and opioid prescribing rates: A population‐based matched cohort study among individuals with and without a history of cancer
Bibliographic record
Abstract
BACKGROUND: Little is known about opioid prescribing among individuals who have survived cancer. Our aim is to examine a predominantly socio-economically disadvantaged population for differences in opioid prescribing rates among cancer survivors compared with matched controls without a prior diagnosis of cancer. METHODS: This was a retrospective population-wide matched cohort study. Starting in 2010, individuals residing in Ontario, Canada, who were 18 to 64 years of age and at least 5 years past their cancer diagnosis were matched to controls without a prior cancer diagnosis based on sex and calendar year of birth. Follow-up was terminated at any indication of cancer recurrence, second malignancy, or new cancer diagnosis. To examine the association between survivorship and the rate of opioid prescriptions, an Andersen-Gill recurrent event regression model was implemented, adjusting for numerous individual-level characteristics and also accounting for the matched design. RESULTS: The rate of opioid prescribing was 1.22 times higher among survivors than among their corresponding matched controls (adjusted relative rate, 1.22; 95% CI, 1.11-1.34). Individuals from lower income quintiles who were younger, were from rural neighborhoods, and had more comorbidities had significantly higher prescribing rates. Sex was not associated with prescribing rates. This increased rate of opioid prescribing was also seen among survivors who were 10 or more years past their cancer diagnosis (compared with their controls). CONCLUSION: This study demonstrates substantially higher opioid prescribing rates among cancer survivors, even long after attaining survivorship. This raises concerns about the diagnosis and management of chronic pain problems among survivors stemming from their cancer diagnosis or treatment. Cancer 2017;123:4286-4293. © 2017 American Cancer Society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".