Methadone as First-line Opioid for the Management of Cancer Pain
Bibliographic record
Abstract
AIM: The aim of this study was to assess the efficacy and adverse effects of methadone when used as first-line therapy in patients that are either receiving low doses of opioids or none. METHODS: Patients with advanced cancer were prospectively assessed. Opioid-naive patients (L-group) were started with methadone at 6 mg/day. Patients receiving weak or other opioids in doses of <60 mg/day of OME (H-group) were started with methadone at 9 mg/day. Methadone doses were changed according to the clinical needs to obtain the most favorable balance between analgesia and adverse effects. Edmonton Symptom Asssement Score (ESAS), Memorial Delirium Assessment Score (MDAS), doses of methadone, and the use of adjuvant drugs were recorded before starting the study treatment (T0), 1 week after (T7), 2 weeks after (T14), 1 month after (T30), and 2 months after (T60). Methadone escalation index percent (MEI%) and in mg (MEImg) were calculated at T30 and T60. RESULTS: Eighty-two patients were assessed. In both groups H and L, there were significant changes in pain and symptom intensity at the different times during the study. Adverse effects as causes of drop-out were minimal. Mean MEImg was 0.09 (SD 0.28) and 0.02 (SD 0.07) at T30 and T60, respectively. MEI% was 1.01 (SD 3.08) and 0.27 (SD 0.86) at T30 and T60, respectively. CONCLUSION: Methadone used as a first-line opioid therapy provided good analgesia with limited adverse effects and a minimal opioid-induced tolerance.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".