The use of tramadol for cancer-associated pain—a systematic review
Bibliographic record
Abstract
BACKGROUND: Tramadol has been used for cancer pain and reported in the literature with varying relative effects compared to other analgesics. To our knowledge, there is no comprehensive systematic review documenting the efficacy/effectiveness and safety of tramadol for cancer-associated pain. The aim of this review is to report on the efficacy/effectiveness and safety of tramadol for cancer-associated pain. METHODS: Ovid MEDLINE, Embase, and Cochrane CENTRAL were searched through September 29, 2023. Articles were included if they reported on tramadol in a multi-arm comparative trial, employing either a randomized controlled trial design or an observational study design with a multivariable or propensity-score matched analysis, and reported on efficacy or safety data pertaining to tramadol. A narrative synthesis was conducted to identify common themes across trials of efficacy and safety endpoints. RESULTS: Eleven studies with 2582 patients were included. Two were cohort studies and nine were randomized controlled trials. There were 20 efficacy endpoints; tramadol was superior in 3, inferior in 4, and neither in 13. There were 80 safety endpoints; tramadol was superior in 9, inferior in 12, and neither in 59. DISCUSSION: Relative to other analgesics, tramadol is neither superior nor inferior. There may exist a different safety profile and therefore an opportunity to provide individualized patient-centered treatment strategies focused on safety and quality of life.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".