Acetaminophen (Paracetamol) Improves Pain and Well-Being in People With Advanced Cancer Already Receiving a Strong Opioid Regimen: A Randomized, Double-Blind, Placebo-Controlled Cross-Over Trial
Bibliographic record
Abstract
PURPOSE: To determine whether adding regular acetaminophen (paracetamol) could improve pain and well-being in people with advanced cancer and pain despite strong opioids. PATIENTS AND METHODS: Participants took acetaminophen for 48 hours and placebo for 48 hours. The order (acetaminophen or placebo first) was randomly allocated. Pain was the primary outcome. Preferences, number of opioid breakthrough doses, overall well-being, nausea and vomiting, drowsiness, constipation, and cold sweats were secondary outcomes. Patients rated themselves daily with visual analog scales (VAS) and a verbal numeric scale (VNS) for pain, all scaled from 0 to 10. RESULTS: Thirty patients completed the trial. The oral opioid was morphine in 23 patients and hydromorphone in seven patients. The median daily opioid dose in oral morphine equivalents was 200 mg (range, 20 to 2,100 mg). Nonsteroidal anti-inflammatory drugs, corticosteroids, or both were used by 16 patients. Pain and overall well-being were better for patients receiving acetaminophen than for those receiving placebo. The mean difference was 0.4 (95% CI, 0.1 to 0.8; P =.03) in VNS for pain, 0.6 (95% CI, -0.1 to 1.3; P =.09) in VAS for pain, and 0.7 (95% CI, 0.0 to 1.4; P =.05) in VAS for overall well-being. More patients preferred the period they took acetaminophen (n = 14) than the period they took placebo (n = 8), but many had no preference (n = 8). There were no differences in the other outcomes. CONCLUSION: Acetaminophen improved pain and well-being without major side effects in patients with cancer and persistent pain despite a strong opioid regimen. Its addition is worth considering in all such patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".