Longterm Effectiveness of Intraarticular Injections on Patient-reported Symptoms in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: We examined the longterm effectiveness of corticosteroid or hyaluronic acid injections in relieving symptoms among persons with knee osteoarthritis (OA). METHODS: Using Osteoarthritis Initiative data, a new-user design was applied to identify participants initiating corticosteroid or hyaluronic acid injections (n = 412). Knee symptoms (pain, stiffness, function) were measured using The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). We used marginal structural models adjusting for time-varying confounders to estimate the effect on symptoms of newly initiated injection use compared to nonusers over 2 years of followup. RESULTS: Among 412 participants initiating injections, 77.2% used corticosteroid injections and 22.8% used hyaluronic acid injections. About 18.9% had additional injection use after initiation, but switching between injection types was common. Compared to nonusers, on average, participants initiating a corticosteroid injection experienced a worsening of pain (yearly worsening: 1.24 points, 95% CI 0.82-1.66), stiffness (yearly worsening: 0.30 points, 95% CI 0.10-0.49), and physical functioning (yearly worsening: 2.62 points, 95% CI 0.94-4.29) after adjusting for potential confounders with marginal structural models. Participants initiating hyaluronic acid injections did not show improvements of WOMAC subscales (pain: 0.50, 95% CI -0.11 to 1.11; stiffness: -0.07, 95% CI -0.38 to 0.24; and functioning: 0.49, 95% CI -1.34 to 2.32). CONCLUSION: Although intraarticular injections may support the effectiveness of reducing symptoms in short-term clinical trials, the initiation of corticosteroid or hyaluronic acid injections did not appear to provide sustained symptom relief over 2 years of followup for persons with knee OA.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".