Effectiveness of vitamin D supplementation on knee osteoarthritis - A target trial emulation study using data from the Osteoarthritis Initiative cohort
Bibliographic record
Abstract
OBJECTIVE: To assess the real-world effectiveness of vitamin D supplementation in patients with knee osteoarthritis (KOA) by replicating a randomized controlled trial (RCT) design in an observational study. METHOD: This study emulated a target trial using data from the Osteoarthritis Initiative (OAI). Eligible participants were ≥45 years, had symptomatic KOA and did not take vitamin D supplements in the past 30 days. A participant can enter the trial more than once. Participants were included in vitamin D group if they took ≥1,000 IU/day for ≥4 days/week in the past 30 days at the first follow-up visit after baseline. The control group did not use vitamin D in the past 30 days. Optimal propensity score matching at 1:1 ratio was performed. The primary outcome was change in knee pain 2 years after baseline measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Secondary outcomes included WOMAC physical function and quantitative joint space width (JSW). Standardized mean difference (SMD) was used to compare the findings with previous RCTs. RESULTS: A total of 236 person-trials in the vitamin D group were pair-matched with a control. Compared to the control group, vitamin D supplementation did not reach significant changes in WOMAC pain (SMD = -0.04, 95%CI [-0.21, 0.13]), physical function and radiographic JSW over 2 years. The SMDs were consistent with the effect sizes reported in previous RCTs. CONCLUSION: Target trial emulation in the OAI cohort demonstrated findings close to published RCTs. This supports the future use of target trial emulation in evaluating other systemic therapies for KOA.
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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 | 0.086 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".