Psychotropic and Opioid Medication Use in Older Patients With Breast Cancer Across the Care Trajectory: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Older patients with breast cancer represent a vulnerable population at higher risk of experiencing distress and pain, as well as medication-related adverse events from pharmacological treatment of these symptoms. The purpose of this study is to estimate the prevalence of psychotropic (anxiolytic, antidepressant, and antipsychotic) and opioid medication use by older women diagnosed with breast cancer. METHODS: This population-based cohort study followed 19,353 women older than 65 years diagnosed with incident, nonmetastatic breast cancer in Quebec, Canada. Data were obtained from provincial, universal health and drug insurance plans covering all medical and pharmaceutical care. Descriptive statistics were calculated for demographic information, breast cancer characteristics, and treatments. Psychotropic and opioid medication use was assessed across the care trajectory: precancer baseline, active care, and first-year survivorship. RESULTS: There was a marked increase in the prevalence of medication use from precancer baseline to active care, followed by a decrease into first-year survivorship. Anxiolytics were used most often across the care trajectory (36.3%, 50.6%, and 44.4% at baseline, active care, and survivorship, respectively). In contrast, antipsychotic and opioid medications were sought primarily during active care (4.5- and 7-fold increases from baseline, respectively), with opioid use during active care increasing dramatically over the study period (9.0% to 40.9% from 1998 to 2010). Unlike other drugs, antidepressant use peaked in active care but persisted into survivorship (14.7%, 22.4%, and 22.3% at baseline, active care, and survivorship, respectively). CONCLUSIONS: A substantial proportion of older patients with breast cancer use psychotropic and opioid medications. The different patterns of medication use represent distress and pain experienced by patients across the care trajectory. Given that medication use in this vulnerable population is associated with an increased risk of adverse events, a multidimensional approach integrating psychological interventions in cancer care may better address psychosocial needs of older patients with breast 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".