196. Illness Perceptions Predict Response to Intra-Articular Steroid Injections in Knee Osteoarthritis
Bibliographic record
Abstract
Background: IA CS injections are widely used to treat pain in knee OA but predictors of response to treatment are poorly understood. Since psychosocial factors are known to influence outcomes in OA in general, we examined selected factors as potential predictors of treatment outcomes in this context. Methods: 141 subjects with painful knee OA underwent baseline assessment prior to injection of the knee joint. As well as physical and disease-related parameters, two groups of psychological characteristics were assessed: firstly illness perceptions, assessed by the Revised Illness Perception Questionnaire (IPQ-R); and secondly pain catastrophizing and depression, assessed by the Pain Catastrophizing Scale (PCS) and depression subscale of the revised Arthritis Impact Assessment Scale (AIMS2), respectively. Symptoms were assessed using the Western Ontario and McMaster Osteoarthritis Index pain subscale at baseline, 3 and 9 weeks, and response to treatment defined as 40% reduction in baseline pain. Characteristics of responders and non-responders at each time point were compared. Logistic regression models, adjusted for relevant disease-related factors, were used to estimate effect size of predictors of response with results expressed as odds ratio (OR) and 95% CI.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".