Baseline Knee Pain Predicts Long-Term Response of Intra-Articular Steroid Injection in Symptomatic Knee Osteoarthritis: Data from OAI
Bibliographic record
Abstract
Objective The current study aims to investigate the factors that could predict response to intra-articular corticosteroid injection (IACI) in patients with knee osteoarthritis (KOA). Methods Data of participants were retrieved from the Osteoarthritis Initiative database. Participants with at least one IACI treatment on single or bilateral knees within the first 5 years of follow-up were retrospectively included. Demographic data, clinical and radiographic variables were collected at both baseline and the first follow-up after IACI treatment. Positive response to IACI treatment was defined as >20% reduction of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score from V0 to V1. All the variables with P < 0.2 after the comparison between the response and non-response groups were included in a multivariable logistic regression model to identify independent response predictive patient-specific valuables. Receiver operating characteristic curves were performed to establish the cutoff values of independent predictors. Results The current study included a total of 385 participants (473 knees), with 155 and 318 knees classified into the response group and non-response group, respectively. Those with satisfied responses to IACI treatment had significantly higher WOMAC pain score ( P < 0.001), disability score ( P = 0.002), and stiffness score ( P = 0.015) at the baseline. Baseline WOMAC pain score showed significant association with positive response to IACI treatment in multivariate logistic analysis and the best cutoff value was 5 points. The rate of analgesics utilization was lower ( P = 0.014) in the response group than the non-response group after the IACI treatment. Conclusion KOA patients with a baseline WOMAC pain score ≥5 are more likely to benefit from IACI treatment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".