Exploring determinants predicting response to intra-articular hyaluronic acid treatment in symptomatic knee osteoarthritis: 9-year follow-up data from the Osteoarthritis Initiative
Bibliographic record
Abstract
BACKGROUND: The weight of recommendation for intra-articular therapies such as hyaluronic acid injections varies from one set of guidelines to another, and they have not yet reached unanimity with respect to the usefulness of intra-articular hyaluronic acid (IAHA) injections for the symptomatic treatment of knee osteoarthritis (OA). Among the reasons for the controversy is that the current literature provides inconsistent results and conclusions about such treatment. This study aimed at identifying determinants associated with a better response to IAHA treatment in knee OA. METHODS: Subjects were selected from the Osteoarthritis Initiative database. Participants were subjects who had radiographic OA, received one IAHA treatment, and had data on demographics and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores at visits before (T0) and after (T1; within 6 months) treatment. Pain was analyzed for demographic, clinical, and imaging characteristics at T0 and change over time (T0 to T1). Subjects with WOMAC pain > 0 at T0 were subdivided into Low, Moderate, and High pain groups based on tertile analysis. Further analyses were done with the High pain group (score ≥ 8), which was divided into responders (improvement in pain ≥ 20%) and nonresponders (unchanged or worsening of pain). RESULTS: Participants (n = 310) received a total of 404 treatments (one per knee). In the Low and Moderate pain groups vs the High pain group, participants had significantly lower score at T0 (p < 0.001), and the Low vs High pain group had significantly lower BMI (p = 0.002), greater joint space width (JSW) (p = 0.010) and knee cartilage volume (p ≤ 0.009), and smaller synovial effusion (p = 0.033). In the High pain group, responders vs nonresponders were usually younger (p = 0.014), with greater cartilage volume in the medial compartment (p = 0.046), a trend toward greater JSW, and a significant improvement in all WOMAC scores (p < 0.001), while nonresponders showed worsening of symptoms. CONCLUSIONS: This study identified reliable predictive determinants that can distinguish patients who could best benefit from IAHA treatment: high levels of knee pain, younger, and less severe structural damage. These could be implemented in clinical practice as a useful guide for physicians.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".