Effect of repeated intra-articular administration of collagen as a treatment for knee osteoarthritis: a case report
Bibliographic record
Abstract
Intra-articular injections aimed at correcting underlying pathophysiological processes and providing pain relief are essential for managing knee osteoarthritis (KOA). Collagen, the primary component of articular cartilage, has a long half-life, making it a promising candidate for intra-articular injections with a low risk of serious side effects. The first of the two cases in this report involved a woman in her early 70s with a 6-year history of persistent left knee pain. The second case involved a woman in her mid-50 s with a >7-year history of right knee pain. Both patients received hyaluronic acid injections every 6 months, totaling 10 injections over 5 years. They were diagnosed with Kellgren-Lawrence grade 2 KOA. The patients received two 3-mL intra-articular injections of 6% collagen, administered at baseline and 6 months later. Improvements in clinical outcomes, including visual analog scale scores and Western Ontario and McMaster Universities Osteoarthritis Index scores, were observed and maintained in both patients, with no side effects reported. In summary, when collagen injections were administered to these two patients with KOA, clinical improvements lasted approximately 6 months, and repeated treatments demonstrated efficacy and safety similar to the initial course.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".