Intra-articular MM-II for the treatment of knee osteoarthritis pain: Efficacy and safety results from a 26-week, phase 2b, placebo-controlled, double-blind, randomized dose-ranging trial
Bibliographic record
Abstract
OBJECTIVE: Determine optimal dose, efficacy, and safety of MM-II, a suspension of large empty liposomes, for knee osteoarthritis (OA) pain. METHOD: A double-blind phase 2b study (NCT04506463) randomized participants 3:3:3:1:3:1 to one intra-articular injection of 1, 3, or 6 mL MM-II or 1, 3, or 6 mL placebo, respectively. Inclusion criteria included age ≥40 years and radiographic and symptomatic knee OA. The primary endpoint was change from baseline in Western Ontario and McMaster Universities OA Index (WOMAC) pain (range, 0-4) 12 weeks post-injection (multiplicity-adjusted). Secondary endpoints included weekly average of daily knee pain (WADP), WOMAC pain at other visits, WOMAC function, patient global assessment (PtGA), and rescue medication use. Safety was assessed by treatment-emergent adverse events (TEAEs). RESULTS: Overall, 396 participants received treatment. In the 3 mL MM-II vs placebo group, WOMAC pain numerically improved at week 12 (least squares mean difference [95% confidence interval], -0.24 [-0.48, 0.00]; unadjusted P = 0.047; multiplicity-adjusted P = 0.085 [primary endpoint not met]). In the same 3 mL group, WADP showed improvements at week 12 (-10.9 [-18.9, -2.8]) lasting through week 26 (-11.8 [-20.4, -3.3]; unadjusted P <0.01 at both time points). Numeric improvements were also seen in WOMAC function from week 8-26, and PtGA at weeks 16 and 26. Rescue medication use with 3 mL MM-II was consistent with reduced pain. Results were numerically superior with 3 mL MM-II vs 1 mL MM-II; 6 mL MM-II was the least efficacious dose. MM-II was well tolerated, with low TEAE incidence. CONCLUSION: MM-II was safe, and the optimal effective dose for the treatment of knee OA pain was 3 mL.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".