Methadone vs. morphine SR for treatment of neuropathic pain: A randomized controlled trial and the challenges in recruitment
Bibliographic record
Abstract
Introduction Accumulating evidence has identified a number of advantages for methadone over other opioids for the treatment of chronic pain including: agonist action at both μ and δ opioid receptors, N-methyl-d-aspartate (NMDA) antagonist activity and the ability to inhibit the reuptake of monoamines. It was hypothesized that with these three mechanisms of action methadone might be a good option for the treatment of neuropathic pain.Methods This was a double-blind randomized controlled trial comparing methadone to controlled-release morphine. The primary objective was to determine whether methadone is clinically inferior versus noninferior to morphine as an analgesic.Results We attempted recruitment at three academic pain centers over a 3-year period. In the end only 14 participants were able to be recruited; 5 withdrew and only 9 completed the trial. This study was underpowered. All participants showed a mean reduction in pain intensity according to the Numeric Rating Scale for Pain Intensity (morphine 5.86 to 4.38, methadone 6.11 to 4.5) and reported pain relief compared to pretreatment, but the sample size was too small for statistical analysis.Discussion Reasons for challenges in recruitment included tight inclusion and exclusion criteria and high participant burden. In addition, there was significant heterogeneity of patients between the three sites, leading to differences in reasons for exclusion. This included seemingly disparate reasons at the different sites, including few participants who were methadone naïve vs. avoidance or fear of opioids. In the end, we were unable to answer the question of the study.
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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 | 0.037 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".