Open-label Use of Anakinra (Kineret) in the Treatment of Patients with Osteoarthritis
Bibliographic record
Abstract
Background: Novel treatments for osteoarthritis (OA) are needed for patients not responding to and/or not tolerating conventional treatments.In this prospective study the usefulness of Anakinra (Kineret) in the treatment of OA was evaluated.Methods: Eleven patients with symptomatic OA were treated with Anakinra (Kineret) over a 2-3 month period.Efficacy of response was determined if patients showed >30% improvement in the Western Ontario and McMaster Universities Osteoarthritis Index and/or Australian/ Canadian Osteoarthritis Hand Index scoring with treatment.Nine of the 11 patients received Kineret intra-articular (IA) injections and 2/11 patients with erosive polyosteoarthritis received Kineret 100 mg subcutaneous injections daily for 30 days.Results: One of the two patients receiving systemic administration of Kineret showed mild efficacy.A total of 21 IA Kineret injections were performed on nine patients.Only 2/5 patients receiving IA Kineret injections into small/medium-sized joints showed efficacy and only 2/5 patients receiving IA Kineret injections into large joints (knees, shoulders) showed efficacybut those patients showing efficacy with large joints injections had >50% improvement.Conclusion: In this study it was found that patients receiving large joint injections were more likely to show greater efficacy than those receiving small joint injections.Therefore, there seems to be a possible benefit of using Kineret (150-200 mg) in the treatment of OA in large joints and may represent an alternative to IA steroid in those patients in whom steroids may be contraindicated.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".