Therapy Switching in Patients Receiving Long-Acting Opioids
Bibliographic record
Abstract
BACKGROUND: Patterns of therapy switching in patients receiving long-acting opioids have not been well documented. OBJECTIVE: To compare therapy switching among patients beginning treatment with controlled-release (CR) oxycodone, transdermal fentanyl, or CR morphine sulfate. METHODS: Using a US healthcare claims database, we identified patients beginning treatment with CR oxycodone, transdermal fentanyl, or CR morphine sulfate between July 1, 1998, and December 31, 1999. We compiled claims for each patient for 6 months following therapy initiation and compared the incidence of therapy switching among the 3 groups. We also estimated total healthcare charges for patients who switched therapy versus those who did not. RESULTS: We identified 1931, 668, and 449 patients beginning therapy with CR oxycodone, transdermal fentanyl, and CR morphine sulfate, respectively; 16.7%, 25.0%, and 35.9%, respectively, had cancer. For patients without cancer, rates of therapy switching at 6 months were 10.6% (CR oxycodone), 19.0% (transdermal fentanyl), and 26.0% (CR morphine sulfate); for those with cancer, rates were 23.8%, 24.6%, and 29.8%, respectively. Multivariate hazard ratios (vs CR morphine sulfate) for therapy switching in patients without cancer were 0.36 (95% CI, 0.27 to 0.47) for CR oxycodone and 0.69 (0.51 to 0.94) for transdermal fentanyl; for those with cancer, corresponding hazard ratios were 0.72 (0.50 to 1.03) and 0.76 (0.50 to 1.16). Total healthcare charges were significantly (p < 0.01) higher for patients who switched therapy than those who did not (23,965 US dollars vs 14,299 US dollars in pts. without cancer; 58,259 US dollars vs 39,618 US dollars for those with cancer). CONCLUSIONS: Patients without cancer who receive CR oxycodone or transdermal fentanyl are less likely to switch therapy than those receiving CR morphine sulfate. Total healthcare charges are higher for patients who switch therapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".