Effects of Hydrocodone Rescheduling on Pain Management Practices Among Older Breast Cancer Patients
Bibliographic record
Abstract
Hydrocodone, a commonly prescribed opioid, was rescheduled from Schedule III to Schedule II in October 2014, imposing stricter prescribing regulations. While prior studies have examined its effects in general populations, its impact on breast cancer patients remains unclear. We evaluated changes in pain management among older women with early-stage breast cancer following this policy change. Using SEER-Medicare data from 2011-2019, we identified a retrospective cohort of 52,792 women aged ≥66 years. We assessed trends in the use of hydrocodone, non-hydrocodone opioids, NSAIDs, and antidepressants before and after rescheduling. Hydrocodone use declined from 55% to 40%, while non-hydrocodone opioid use increased from 43% to 50%. Multivariable logistic regression adjusted for demographic and clinical factors confirmed a significant decrease in hydrocodone use (AOR: 0.81, 95% CI: 0.75-0.86) and an increase in non-hydrocodone opioid use (AOR: 1.25, 95% CI: 1.21-1.30). Hydrocodone dosage also declined, while non-hydrocodone opioid dosages remained stable. No significant changes were observed in NSAID or antidepressant use. These findings suggest that hydrocodone rescheduling significantly altered opioid prescribing patterns, reducing hydrocodone use and prompting a shift toward alternative opioids. Further research is warranted to evaluate the appropriateness and outcomes of such shifts in cancer pain management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".