Opioid use among cancer survivors: A call to action for oncology and primary care providers
Bibliographic record
Abstract
We thank Sutradhar et al for their valuable work on a critically important topic and herein provide several comments regarding their recent publication. 1 This retrospective study of a predominantly disadvantaged Canadian population of 17,202 individuals found a higher opioid prescribing rate among cancer survivors, defined as individuals who are at least 5 years after diagnosis, compared with 8601 matched controls without a prior cancer diagnosis.After multivariable adjustment, the opioid prescribing rate was 1.22 times higher among survivors compared with controls.The difference in opioid prescribing rates was especially pronounced among survivors of noncolorectal gastrointestinal, gynecologic, and lung cancers.Surprisingly, a higher prescribing rate persisted even among survivors >10 years beyond diagnosis.Cancer survivors face a wide variety of physical and psychological challenges that may prevail for years, even decades, after treatment.[2][3][4][5][6] When examining the prevalence of health conditions among survivors using retrospective data, it often is unclear whether these conditions existed before the diagnosis of cancer, emerged during treatment, or are due to the late effects of cancer treatment.Unfortunately, the study by Sutradhar et al does not provide insights into the initiation of the opioids (including which provider prescribed the drugs and for what indication [ie, cancer or otherwise]) or assess differences in opioid prescriptions by cancer stage or treatment type.Furthermore, it is not clear from the published study why survivors continued to receive opioid medications (ie, whether they were receiving the drugs as part of a thoughtful, ongoing pain management plan or whether these prescriptions were inadvertently continued as "remnants" from cancer treatment).Addressing these questions is of vital importance when trying to understand how targeted interventions to potentially reduce opioid prescriptions for cancer survivors may be developed.Cancer survivors are at risk of persistent pain, which may be due to a variety of treatment exposures, including chemotherapy-related neuropathy, radiation-related fibrosis, and surgery (ie, amputation or adhesions).7,8 In addition to Cancer
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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 | 0.037 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.046 | 0.038 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".