Opioid use in long term cancer survivors.
Bibliographic record
Abstract
6520 Background: Our research team previously found that the rate of opioid use in cancer patients surviving at least 5 years beyond diagnosis was 1.2 times higher compared with age-sex matched controls without cancer. The purpose of this study was to evaluate the factors associated with opioid use after 5 years of survival from cancer diagnosis. Methods: We conducted a retrospective cohort study using linked provincial administrative data. Patients were aged 24-70 and economically disadvantaged, making them eligible for government funded pharmacare. The index date was defined as the 5 year anniversary from the diagnosis date. Patients were accrued continuously between April 1, 2010 and March 31, 2015 on their index date. Those with any evidence of recurrence (resuming anti-cancer therapy, palliative care) were excluded. Patients were observed until death, relapse or end of data accrual. The main outcome was opioid prescription rate after index date. The main exposures were opioid use prior to diagnosis date, opioid use between diagnosis date and index date (none, continuous, at diagnosis only, other), certain cancer surgeries (e.g. thoracotomy) and chemotherapy agents known to cause neuropathy. A negative binomial regression model was used to estimate the relative rates of opioid use after index date. Results: Our cohort included 7,431 individuals. The factors most strongly associated with a higher rate of opioid use after index date was continuous opioid use between diagnosis and index date. The RR was 63.4 (95% CI 39.4-102.1) for those with no pre-diagnosis opioid use and 76.6 (95% CI 40.7-144.0) for those with pre-diagnosis opioid use. The only group with no increased risk used opioids only at diagnosis and had no prior use. Surgery was not significant. Chemotherapy was not significant with the exception of those who used opioids initially. A history of depression, comorbidity and more than 2 years of diabetes were also associated with higher risk. Conclusions: Cancer patients who use opioids continuously after diagnosis are at increased risk of continued use after 5 years of survival. Further work is needed to understand the reasons for ongoing use after diagnosis. Increased attention is needed to ensure safe prescribing for this group to minimize issues with dependence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".