Transdermal fentanyl for improvement of pain and functioning in osteoarthritis: A randomized, placebo‐controlled trial
Bibliographic record
Abstract
OBJECTIVE: Although common treatments for osteoarthritis (OA) pain, such as nonsteroidal antiinflammatory drugs (NSAIDs), simple analgesics, and weak opioids, provide relief in some cases, they fail to control pain or are poorly tolerated in many cases. Strong opioids have been used to successfully treat several types of noncancer pain but have rarely been tested in controlled studies. Therefore, we tested the effects of transdermal fentanyl (TDF) in patients with moderate-to-severe OA pain, in a placebo-controlled study. METHODS: The cohort comprised patients with radiologically confirmed OA of the hip or knee (meeting the American College of Rheumatology criteria) requiring joint replacement and with moderate-to-severe pain that had been inadequately controlled by weak opioids. The patients were randomized to receive TDF or placebo for 6 weeks after a 1-week pretreatment run-in phase. During study treatment, previously prescribed NSAIDs and simple analgesics were continued, but weak opioids were discontinued. All patients had access to paracetamol and metoclopramide. Pain was recorded on a visual analog scale (VAS), and function was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). RESULTS: Data were available for 399 patients (202 receiving TDF, 197 receiving placebo), of whom 199 (50%) completed the study. TDF provided significantly better pain relief than placebo, as demonstrated by the primary outcome measure (area under the curve for VAS scores -20 in the TDF group versus -14.6 in the placebo group; P = 0.007). TDF was also associated with significantly better overall WOMAC scores and pain scores. The most common adverse events were nausea, vomiting, and somnolence, and these occurred more often in the TDF group. CONCLUSION: TDF can reduce pain and improve function in patients with knee or hip OA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".