P077 Can we predict who responds to intra-articular corticosteroid injection in knee arthritis? A clinical trial comparing response in inflammatory arthritis and osteoarthritis
Bibliographic record
Abstract
Abstract Background/Aims The global burden of knee arthritis is significant and rising. Patients can experience pain and disability over a long duration. Intra-articular corticosteroid injections (IACI) can reduce inflammation and pain in arthritis. However, there is disparity in pain reduction and efficacy duration following IACI. We aimed to identify factors that influence IACI response in knee osteoarthritis (OA) and inflammatory arthritis (IA), to aid stratification of future patients. Methods In this multi-centre prospective study, participants with knee OA and IA (rheumatoid, psoriatic or mixed arthritis on standard treatment) were recruited. At baseline, demographics, DAS28, PsARC, painDETECT and Western Ontario and McMasters Universities Arthritis Index (WOMAC) for pain, stiffness and function were completed. We classified painDETECT scores with ≥19 “sensitised” and ≤18 “non-sensitised” for pain. Sonographic assessment and IACI was performed, with synovitis/effusion categorised dichotomously (present/absent). Two musculoskeletal radiology consultants graded knee radiographs as low Kellgren-Lawrence grade 0-2 (LKLG) or high grade 3-4 (HKLG). At 3 months, all questionnaires were repeated. IACI response was defined as 20% improvement from baseline WOMAC pain. Differences between responders/non-responders were assessed using Wilcoxon (continuous variables) and Fisher’s exact tests (categorical variables). Analysis was conducted in R. Results There were 129/136 patients recruited who had IACI: 89 were analysed (62 OA, 27 IA) (Table 1), with 40 lost to follow-up. Irrespective of arthritis pathology, odds of IACI response among sensitised versus non-sensitised participants was 0.33(p = 0.051). In the LKLG-OA cohort, responders had significantly lower baseline painDETECT scores than non-responders (p = 0.015); 10/14 (71%) non-sensitised responded to IACI, compared with 0/6 (0%) of the sensitised. No difference in baseline painDETECT scores between responders and non-responders in the HKLG-OA cohort(p = 0.50) or in either inflammatory cohort(LKLG p = 0.86, HKLG p = 0.77) was detected. In the OA-synovitis/effusion cohort, responders had lower baseline painDETECT scores; while this was not statistically significant (p = 0.13), 0/6 (0%) of sensitised participants responded, versus 17/33 (52%) non-sensitised patients. Conclusion Our study suggests imaging and clinical measures can predict IACI response, with pain sensitisation conferring lower likelihood of response, particularly in OA with LKLG. Following validation studies, design of a predictive clinical model could allow patient stratification for personalised treatment, reducing IACI waiting times and optimising resource allocation. Disclosure A. Paterson: None. K. Feather: None. A. Lambarth: None. M. Siebachmeyer: None. V. Ejindu: None. F. Howe: None. A. Rudnicka: None. A. Ezeonyeji: None. R. Ramsden: None. N. Sofat: None.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".