Intra-articular collagenase injection increases range of motion in a rat knee flexion contracture model
Bibliographic record
Abstract
Objectives: A knee joint contracture, a loss in passive range of motion (ROM), can be caused by prolonged immobility. In a rat knee immobilization flexion contracture model, the posterior capsule was shown to contribute to an irreversible limitation in ROM, and collagen pathways were identified as differentially expressed over the development of a contracture. Collagenases purified from Clostridium histolyticum are currently prescribed to treat Dupuytren’s and Peyronie’s contractures due to their ability to degrade collagen. The potential application of collagenases to target collagen in the posterior capsule was tested in this model. Materials and methods: Rats had one hind leg immobilized, developing a knee flexion contracture. After 4 weeks, the immobilization device was removed, and the rats received one 50 µL intra-articular injection of 0.6 mg/mL purified collagenase. Control rats were injected with only the buffer. After 2 weeks of spontaneous remobilization following the injections, ROM was measured with a rat knee arthrometer, and histological sections were immunostained with antibodies against rat collagen types I and III. Results/conclusion: Compared with buffer-injected control knees, collagenase-treated knees showed increased ROM in extension by 8.0°±3.8° ( p -value <0.05). Immunohistochemical analysis revealed an increase in collagen type III staining ( p <0.01) in the posterior capsule of collagenase-treated knees indicating an effect on the extracellular matrix due to the collagenase. Collagen I staining was unchanged ( p >0.05). The current study provides experimental evidence for the pharmacological treatment of knee flexion contractures with intra-articular collagenase injection, improving the knee ROM. Keywords: joint, contracture, collagen, immobilization range of motion
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".