Attitudes and Practices of Pediatric Oncologists Regarding Methadone Use in the Treatment of Cancer-related Pain
Bibliographic record
Abstract
Methadone is effective in the treatment of cancer-related pain in adults. Pediatric oncologists may be reluctant to use methadone, given the paucity of existing research and a lack of familiarity with its use. This study's purpose was to assess pediatric oncologists' experience, comfort and practice of methadone prescription, and determine interest in and appropriate venues for education on methadone. A 22-item survey was sent by electronic mail to 1912 practicing pediatric oncologists. Six hundred thirty-one pediatric oncologists (33%) responded to the survey. Seventy-two percent of respondents reported they prescribe methadone to <10% of their patients receiving opioids. Physicians practicing ≥10 years (84% vs. 76%, P=0.01), at centers that see ≥100 new patients per year (86% vs. 76%, P=0.003), or who have received prior education on methadone (89% vs. 54%, P<0.001) were more likely to have prescribed methadone. The primary reasons respondents did not utilize methadone included a lack of knowledge of methadone's pharmacodynamics (39%), effectiveness (39%), and/or dosing equivalence (34%). Perceived competence with dose equivalence, belief that methadone is effective, and working in a division where >20 patients per year died were all independently associated with having prescribed methadone to >10% of patients on opioids. Eighty-five percent of respondents would like additional education on methadone. Many pediatric oncologists lack experience and education in the use of methadone. Formal education initiatives should be implemented to enhance pediatric oncologists' comfort and expertise in methadone use.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".