The Opioid Rotation Ratio (ORR) to transdermal fentanyl (TDF) in cancer patients.
Bibliographic record
Abstract
181 Background: Cancer patients frequently undergo opioid rotation (OR) for uncontrolled pain or opioid induced neurotoxicity. TDF is one of the most common opioids prescribed to cancer patients. However, the accurate ORR from other opioids to TDF is unknown and various currently used methods result in a wide variation of ORRs. Our aim was to determine the ORR of morphine equivalent daily dose (MEDD) to TDF when correcting for MEDD of breakthrough opioids (net MEDD) in cancer outpatients. Methods: We reviewed records of 22,532 consecutive patient visits at our Supportive Care Center in 2010-13 for OR from to TDF by a palliative medicine specialist. Data regarding Edmonton Symptom Assessment Scale (ESAS) and MEDD were collected in patients who returned for follow up within 5 weeks. Linear regression analysis was used to estimate the ORR between TDF dose and net MEDD (MEDD prior to OR minus MEDD of breakthrough opioid used along with TDF after OR). Successful OR was defined as 2-point or 30% reduction in pain score and continuation of the new opioid at follow up. Results: 129 patients underwent OR to TDF from other opioids. The mean age was 56 years, 59% male, and 88% had advanced cancer. The median time between OR and follow up was 14 days. Uncontrolled pain (80%) was the most frequent reason for OR and 59% had a successful OR with significant improvement in ESAS pain, constipation, and symptom distress scores. In 101 patients with OR and no worsening of pain at follow up, the median ORR (range) from net MEDD to TDF mg/day was .01 (-0.02-0.04) and correlation of TDF dose to net MEDD was .77 (P < .0001). The ORR was not significantly impacted by variables such as mucositis, serum albumin, and body mass index (BMI). The ORR of .01 suggests that MEDD of 100mg is equivalent to 1mg TDF/day or 40mcg/hour TDF patch (1000mcg/24hours). Conclusions: The median ORR from MEDD to TDF mg/day is .01 and the ORR from MEDD to TDF mcg/hour patch is 0.4. Further validation studies are needed. [Table: see text]
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".