The Opioid Rotation Ratio of Hydrocodone to Strong Opioids in Cancer Patients
Bibliographic record
Abstract
PURPOSE: Cancer pain management guidelines recommend initial treatment with intermediate-strength analgesics such as hydrocodone and subsequent escalation to stronger opioids such as morphine. There are no published studies on the process of opioid rotation (OR) from hydrocodone to strong opioids in cancer patients. Our aim was to determine the opioid rotation ratio (ORR) of hydrocodone to morphine equivalent daily dose (MEDD) in cancer outpatients. PATIENTS AND METHODS: We reviewed the records of consecutive patient visits at our supportive care center in 2011-2012 for OR from hydrocodone to stronger opioids. Data regarding demographics, Edmonton Symptom Assessment Scale (ESAS), and MEDD were collected from patients who returned for follow-up within 6 weeks. Linear regression analysis was used to estimate the ORR between hydrocodone and MEDD. Successful OR was defined as 2-point or 30% reduction in the pain score and continuation of the new opioid at follow-up. RESULTS: Overall, 170 patients underwent OR from hydrocodone to stronger opioid. The median age was 59 years, and 81% had advanced cancer. The median time between OR and follow-up was 21 days. We found 53% had a successful OR with significant improvement in the ESAS pain and symptom distress scores. In 100 patients with complete OR and no worsening of pain at follow-up, the median ORR from hydrocodone to MEDD was 1.5 (quintiles 1-3: 0.9-2). The ORR was associated with hydrocodone dose (r = -.52; p < .0001) and was lower in patients receiving ≥40 mg of hydrocodone per day (p < .0001). The median ORR of hydrocodone to morphine was 1.5 (n = 44) and hydrocodone to oxycodone was 0.9 (n = 24). CONCLUSION: The median ORR from hydrocodone to MEDD was 1.5 and varied according to hydrocodone dose.
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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 | 0.001 | 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".