Inflammatory Cytokine Profiles Associated With Chondral Damage in the Anterior Cruciate Ligament–Deficient Knee
Bibliographic record
Abstract
PURPOSE: Patients with chronic anterior cruciate ligament (ACL) deficiency are at high risk of articular cartilage damage and the development of osteoarthritis (OA). It has been hypothesized that biochemical factors, such as cytokines, contribute to the process. The purpose of our study was to determine the concentrations of potentially chondrodestructive and chondroprotective cytokines in the chronic ACL-deficient knee, and to determine if the cytokine profile or other factors correlated with the amount of chondral damage present in the knee. TYPE OF STUDY: A consecutive series of patients who consented to the Institutional Review Board-approved study. METHODS: Synovial fluid lavages were obtained from 31 patients with ACL-deficient knees. Four patients had lavages aspirated from their contralateral normal knee. These lavages were analyzed for interleukin (IL)-1alpha, IL-1beta, IL-1ra, and tumor necrosis factor (TNF)-alpha. At arthroscopy, the amount of chondral damage was graded based on the Outerbridge classification. RESULTS: Concentrations of chondrodestructive IL-1beta and TNF-alpha were significantly higher in patients with ACL ruptures than in the contralateral normal knees. The more severe the chondral damage, the higher the concentration of IL-1beta and TNF-alpha. Chondroprotective cytokine concentrations decreased with increasing grades of chondral damage. We found a linear correlation between the severity of chondral damage and the time from injury (r2 = .954). CONCLUSIONS: A difference was seen in the cytokine profiles between the normal and injured knees. This difference varied based on the severity of chondral damage, which was associated with time from injury. CLINICAL RELEVANCE: Cytokine levels are associated with chondral damage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".