Assessment of Pain and Activity Using an Electronic Pain Diary and Actigraphy Device in a Randomized, Placebo‐Controlled Crossover Trial of Celecoxib in Osteoarthritis of the Knee
Bibliographic record
Abstract
OBJECTIVE: The primary goal was to determine whether a composite measure of pain and activity is a more responsive assessment of analgesic effect than pain alone or activity alone in patients with osteoarthritis (OA) of the knee. DESIGN: We conducted a randomized, double-blind, placebo-controlled, 2-period, crossover study of celecoxib vs. placebo in subjects with chronic pain due to knee OA. Patients with knee OA and baseline pain intensity score ≥4 on a 0-10 numerical rating scale (NRS) before each period were randomized. Pain endpoints included in-clinic pain score (24-hour and 1-week recall), daily paper diary pain score, current pain on an electronic pain diary (each on NRS), and WOMAC pain subscale. Activity measures included WOMAC function subscale and actigraphy using a device. Three composite pain-activity measures were prespecified. RESULTS: Sixty-three patients were randomized and 47 completed the study. The WOMAC pain subscale was the most responsive of all five pain measures. Pain-activity composites resulted in a statistically significant difference between celecoxib and placebo but were not more responsive than pain measures alone. However, a composite responder defined as having 20% improvement in pain or 10% improvement in activity yielded much larger differences between celecoxib and placebo than with pain scores alone. Actigraphy was more responsive than the WOMAC function scale, possibly due to lower placebo responsiveness. CONCLUSION: We have identified composite pain-activity measures that are similarly or more responsive than pain-alone measures in patients with OA. Further research is warranted to determine the optimal method for computing these composites.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".