Placebo Effect Sizes in Clinical Trials of Knee Osteoarthritis Using Intra‐Articular Injections of Biologic Agents
Bibliographic record
Abstract
OBJECTIVE: Patients with knee osteoarthritis rely on symptomatic treatments, in which up to 75% of the pain reduction can be attributed to the placebo effect. This effect may vary based on treatment type (eg, biologics vs nonbiologic injection) and route of administration (eg, intra-articular vs topical vs oral). The placebo effect is an integral part of treatment effect size calculation; thus, network analyses comparing efficacies of different treatments may be inaccurate. The objective of this study was to test the hypothesis that placebo effects differ between treatment types and route of delivery. METHODS: A systematic literature search was conducted in August 2019. Randomized trials comparing pain outcomes of oral, topical, or intra-articular placebo interventions to active treatments were included. The outcome measure of interest was change in pain scores from baseline. Data were stratified by length of follow-up and treatment subcategory. RESULTS: A total of 129 articles were included with 9,218 patients receiving placebo treatments. Reduction in pain from baseline occurred in 93% of the subcategory data points. Biologic intra-articular placebo injections had the greatest pain reduction at one month (mean ± SD visual analog scale -32.2 ± 24.6; mean ± SD Western Ontario and McMaster Universities Arthritis Index -16.3 ± 3.81). At one month and two months, placebo intra-articular injections had a greater pain reduction than oral placeboes (P ≤ 0.01). CONCLUSION: The robust placebo effect is influenced by the active treatment category and changes over time. The variation in placebo response despite analogous placebo methodologies implies using network meta-analyses to compare treatments from different active treatment categories by evaluating the change from placebo is inaccurate.
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 | 0.267 | 0.520 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".