Synovial and cartilage responsiveness to peri‐operative hyaluronic acid ± dexamethasone administration following a limited injury to the rabbit stifle joint
Bibliographic record
Abstract
Posttraumatic osteoarthritis (PTOA) can develop after an injury to the knee. Previous studies have indicated that an intra-articular (IA) injection of the potent glucocorticoid dexamethasone (DEX) may significantly prevent induction of PTOA. The aim of the present study was to investigate the effectiveness of a single IA injection of hyaluronic acid (HA), alone and in combination with DEX following a localized intra-articular injury as a PTOA-preventing treatment option. An established rabbit model of surgical injury consisting of dual intra-articular (IA) drill holes in a non-cartilaginous area of the femoral notch near the origin of the anterior cruciate ligament (ACL) to allow for bleeding into the joint space was used. Immediately following surgery, subjects were treated with HA, HA + DEX, or received no treatment. An uninjured control group was used for comparison (N = 5/group). Rabbits were sacrificed and investigated at 9 weeks post-injury. At 9 weeks post-injury, there was a significant protective capacity of the single IA treatment of DEX + HA on the histological grade of the synovial tissue, and some variable location-specific effects of HA alone and HA + DEX interactions on cartilage damage. Thus, it is possible that co-treatment with HA may interfere with the effectiveness of the DEX. In vitro friction testing indicated that DEX did not interfere with the lubricating ability of HA or synovial fluid on cartilage. These results suggest that a single IA administration of HA in combination with DEX following an IA injury is not recommended for inhibition of PTOA progression in this model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".