Extended‐release opioids in the management of cancer pain: A systematic review of efficacy and safety
Bibliographic record
Abstract
Despite the increased availability of strong analgesics and evidence-based recommendations for pain management, under-treatment of cancer-related pain is still common. Extended-release (ER) opioids, in contrast to immediate-release opioids, provide prolonged analgesia. In this review, we aimed to compare the efficacy and safety of ER opioid analgesics in managing moderate-to-severe pain in patients with cancer. We identified randomized controlled trials (RCTs) and controlled observational studies that compared ER opioids in cancer pain by searching several databases, including MEDLINE, EMBASE and the Cochrane Library. Two independent reviewers screened and evaluated retrieved records to select relevant studies. We dually assessed the risk of bias for included studies and evaluated the overall strength of evidence for six critical outcomes using Grading of Recommendations Assessment, Development and Evaluation level of evidence. A total of three double-blind RCTs (comparative efficacy and adverse events), two non-blinded RCTs and four observational studies (comparative adverse events) were included in this review. All randomized trials and one observational study were of high risk of bias, and three observational studies of unclear risk of bias. The level of evidence for the selected efficacy and safety outcomes was low and very low. We synthesized the findings qualitatively because of the paucity of relevant studies as well as variable study design and quality. This systematic review indicates no substantial differences in efficacy and frequent adverse events among ER opioids for cancer pain. The body of evidence, however, is limited to few comparisons and fraught with methodological shortcomings.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".