The opioid rotation ratio of strong opioids to transdermal fentanyl in cancer patients
Bibliographic record
Abstract
BACKGROUND: Transdermal fentanyl (TDF) is 1 of the most common opioids prescribed to patients with cancer. However, the accurate opioid rotation ratio (ORR) from other opioids to TDF is unknown, and various currently used methods result in wide variation of the ORR. The objective of this study was to determine the ORR of the oral morphine equivalent daily dose (MEDD) to the TDF dose when correcting for the MEDD of breakthrough opioids (the net MEDD) in cancer outpatients. METHODS: The records of 6790 consecutive patients were reviewed at the authors' supportive care center from 2010 to 2013 to identify those who underwent rotation from other opioids to TDF. Data regarding Edmonton Symptom Assessment Scale scores and MEDDs were collected for patients who returned for a follow-up visit within 5 weeks. Linear regression analysis was used to estimate the ORR between the TDF dose and the net MEDD (the MEDD before opioid rotation [OR] minus the MEDD of the breakthrough opioid used along with TDF after OR). RESULTS: In total, 129 patients underwent OR from other opioids to TDF. The mean patient age was 56 years, 59% were men, and 88% had advanced cancer. Uncontrolled pain (80%) was the most frequent reason for OR. In 101 patients who underwent OR and had no worsening of pain at follow-up, the median ORR from net MEDD to TDF (in mg per day) was 0.01 (range, -0.02 to 0.04), and the correlation coefficient of the TDF dose to the net MEDD was 0.77 (P < .0001). The ORR was not significantly impacted by body mass index or serum albumin. The ORR of 0.01 suggests that an MEDD of 100 mg is equivalent to 1 mg TDF daily or approximately 40 micrograms per hour of TDF (1000 micrograms/24 hours). CONCLUSIONS: The median ORR from MEDD to TDF in mg per day was 0.01. These results warrant further studies.
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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 | 0.000 | 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".