Low-Dose Morphine versus High-Dose Tramadol for management of moderate cancer pain: a comparative study
Bibliographic record
Abstract
Introduction: The treatment of pain in cancer patients following the guidelines outlined by World Health Organisation has been found to be feasible and effective.The guidelines recommend a sequential threestep analgesic ladder for treatment of pain but there is a lack of conclusive data regarding the management of moderate pain with step II weak opioids or low-dose step III strong opioids.Methods: In total, Eighty two adult patients with moderate cancer pain were included in this randomised controlled study to receive either low dose morphine or high dose tramadol.The primary outcome was the number of responder patients where the response was defined as patients with a 20% reduction in pain intensity on the numerical rating scale.Results: The primary outcome occurred in 85.8% of the low-dose morphine and in 57.8% of the tramadol group (odds risk, 4.41; 95% CI,P<.001).The percentage of responder patients was found to be higher in the low-dose morphine group in this study.Clinically meaningful (>30%) and highly meaningful (>50%) pain reduction from baseline was significantly higher in the low-dose morphine group (P <.001).Due to inadequate analgesia a change in the treatment process occurred more frequently in the tramadol group.The general condition of patients, which was based on the Edmonton Symptom Assessment System (ESAS) overall symptom score, was better in the morphine group.Adverse effects were similar in both groups.Conclusion: Moderate cancer pain can be managed significantly better by low dose morphine than tramadol with early onset of action and similar level of adverse effects.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".