Fixed-effect or random-effect models? A methodological reappraisal of subgroup analyses in mesenchymal stem cell therapy for knee osteoarthritis
Bibliographic record
Abstract
We commend Cao et al. for their systematic review demonstrating the efficacy of intra-articular mesenchymal stem cell (MSC) therapy in alleviating pain and improving function in patients with non-surgical knee osteoarthritis (OA). However, we reanalyzed their subgroup analyses to evaluate the methodological implications of statistical model selection (fixed-effect vs. random-effect models) on result reliability. In dose-stratified analyses, Cao et al. applied fixed-effect models to low-dose (I 2 = 0%) and high-dose (I 2 = 80%) MSC subgroups. Upon reanalysis using random-effect models, the high-dose group showed no statistically significant differences in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total scores compared to the control group at 6 months [MD = 8.75; 95% CI (−2.10, 19.61); P = 0.11] or 12 months [MD = 12.68; 95% CI (−4.96, 30.32); P = 0.16], contrasting with Cao et al.’s original findings. The low-dose subgroup, with no heterogeneity, yielded identical results across both models. Similarly, in cell-source stratification (adipose-derived MSCs [ADMSCs] vs. bone marrow-derived MSCs [BM-MSCs]), reanalysis of ADMSCs using random-effect models demonstrated significant 6-month WOMAC improvement [MD = 9.32; 95% CI (3.73, 14.92); P = 0.001] but non-significant 12-month differences [MD = 12.90; 95% CI (−1.76, 27.55); P = 0.08], diverging from Cao et al.’s conclusions. BM-MSCs results remained consistent due to negligible heterogeneity (I 2 = 0%). These findings underscore that fixed-effect models artificially narrow confidence intervals in heterogeneous populations, overestimating clinical significance. Our results align with Cochrane guidelines, emphasizing that random-effect models better accommodate inter-study diversity, yielding conservative and clinically generalizable estimates. This critique reinforces the necessity of transparent statistical model selection in meta-analyses, particularly when subgroup heterogeneity may influence therapeutic interpretations.
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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 | 0.442 | 0.596 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".