Changes of pain intensity results with different follow-up times in randomized controlled trials of osteoarthritis
Bibliographic record
Abstract
Objectives: The aim of this Thesis was to conduct comprehensive analysis of efficacy data for pain in randomized controlled trial RCTs about Celecoxib in osteoarthritis (OA). The ultimate purpose of this study is to improve long-term management of pain for patients suffering from OA by guiding clinical decision making, and to create evidence that will inform design of future RCTs about OA. Material and Methods: This was a methodological study in which publicly available data from RCTs were analyzed. RCTs analyzing the effects of 200 mg celecoxib vs. placebo on pain intensity with the Visual Analog Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score were included. Random effect meta-analysis was used for different pain outcome measures and different follow-up times. Standardized mean differences were used to report the data. Results: We found a decreasing trend of a numerical indicator for efficacy of celecoxib for treatment of pain in RCTs comparing Celecoxib 200 mg to Placebo and reported pain results with the VAS and WOMAC scale. Standardized mean differences remained relatively constant with VAS and WOMAC over most follow-up times. The later follow-up times showed a decreased SMD for VAS at 13 weeks as well as for WOMAC with 24 weeks. Conclusion: Our data indicates that efficacy of celecoxib 200 mg could decrease over longer follow-up times. Future trials should include assessment at longer follow-up times for adequate assessment of efficacy and safety of celecoxib.
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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 | 0.190 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.023 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".