Methadone to treat chemotherapy-induced peripheral neuropathy (METACIN): study protocol
Bibliographic record
Abstract
RATIONALE: Chronic chemotherapy-induced peripheral neuropathy (CIPN) affects 70% of cancer patients, causing neuropathic pain. Duloxetine is the most recommended treatment for CIPN per most guidelines. However, Methadone, an alternative and effective treatment for refractory neuropathic cancer pain has been under-recognized and under-studied in patients with CIPN. PARTICIPANTS: Adult patients with cancer and life expectancy greater than 12 weeks who have >grade 1 CIPN based on National Cancer Institute Common Toxicity Criteria for Adverse Events version 5.0 grading scale lasting ≥3 months beyond chemotherapy completion. INTERVENTION: A triple-blind, double-dummy randomized controlled trial, participants randomized to either methadone or duloxetine, followed weekly over 5 weeks with dose titration. OUTCOMES: Primary outcome is the efficacy of methadone versus duloxetine in reducing average pain intensity from baseline to study end. Secondary outcomes include improvements in functional and quality-of-life interference. Exploratory outcomes include proportion of participants achieving ≥30% or ≥50% pain reduction, patient-reported global impression of change, incidence of adverse events, and methadone dose escalation over a 24-week follow up period. ANTICIPATED IMPACT: This study will determine if methadone is a viable treatment for CIPN; a very common, distressing, and debilitating condition that otherwise has limited treatment options. CLINICAL TRIAL REGISTRATION: www.clinicaltrials.gov identifier is NCT05786599.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.092 | 0.017 |
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".