The conversion ratio for opioid rotation from hydrocodone to other strong opioids in cancer patients.
Bibliographic record
Abstract
164 Background: Cancer pain is initially treated with intermediate strength analgesics such as hydrocodone and subsequently escalated to stronger opioids. There are no studies on the process of opioid rotation (OR) from hydrocodone to strong opioids in cancer patients. Our aim was to determine the conversion ratio (CR) for OR from hydrocodone to morphine equivalent daily dose (MEDD) in cancer outpatients. Methods: We reviewed records of 3,144 consecutive patient visits at our Supportive Care Center in 2011-12 for OR from hydrocodone to stronger opioids. Data regarding demographics, Edmonton Symptom Assessment Scale (ESAS), and MEDD were collected in patients who returned for follow up within 6 weeks. Linear regression analysis was used to estimate the CR 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: 170/3,144 patients underwent OR from hydrocodone to stronger opioid. 72% were white, 56% male, and 81% had advanced cancer. The median time between OR and follow up was 21 days. 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 CR (Q1-Q3) from hydrocodone to MEDD was 1.5 (0.9-2) and hydrocodone dose to MEDD correlation was.52 (P<0.0001). The correlation of CR with hydrocodone dose was -0.52 (P<0.0001). The median CR of hydrocodone to MEDD was 2 in patients receiving < 40mg of hydrocodone/day and 1 in patients receiving ≥ 40mg of hydrocodone/day (P<0.0001). The median conversion ratio of hydrocodone to morphine was 1.5 (n=44) and hydrocodone to oxycodone was 0.9 (n=24). Conclusions: Hydrocodone is 1.5-fold stronger than Morphine. The median conversion ratio from hydrocodone to MEDD varied according to hydrocodone dose/day. [Table: see text]
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 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 | 0.013 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".