The Opioid Rotation Ratio (ORR) from transdermal fentanyl (TDF) to strong opioids in cancer patients.
Bibliographic record
Abstract
182 Background: Despite being the most frequently prescribed strong opioid by oncologists, there is a lack of knowledge of the accurate the opioid rotation ratio (ORR) from transdermal fentanyl (TDF) to other strong opioids in cancer patients. Opioid rotation (OR) from TDF to other strong opioids is performed very frequently in cancer patients for uncontrolled pain or opioid induced neurotoxicity (OIN). The aim of our study was to determine the ORR of TDF to other strong opioids, as measured by morphine equivalent daily dose (MEDD). Methods: In this ad hoc analysis, we reviewed 2471 consecutive patient visits to the supportive care center of a tertiary cancer center in 2008 for an OR from TDF to other strong opioids by a palliative medicine specialist. Information regarding demographics, Edmonton Symptom Assessment Scale (ESAS), and MEDD were collected in patients who followed-up within 6 weeks. Linear regression analysis was used to estimate the ORR between TDF dose and net MEDD (MEDD after OR minus MEDD of breakthrough opioid used along with TDF before OR). Successful OR was defined as 2-point or 30% reduction in pain score and continuation of the new opioid at follow up. Results: 47/2471 patients underwent OR from TDF to other opioids and followed-up within 6 weeks. The median age was 54 years, 53% were male, and 77% had advanced cancer. The median time between OR and follow up was 14 days. Uncontrolled pain (83%) followed by OIN (15%) were the most frequent reasons for OR and 77% had a successful OR with significant improvement in ESAS pain and symptom distress scores. In patients with OR and no worsening of pain at follow-up (n = 41), the median ORR (range) from TDF mg/day to net MEDD was 100 (12.5-217), TDF mcg/hour to net MEDD was 2.4 (0.3-5.2), and correlation of TDF dose to net MEDD was .60 (P < 0.0001). Conclusions: The median ORR from TDF mg/day to MEDD is 100 and from TDF mcg/hour to MEDD is 2.4. Further validation studies are needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".