Effects of ACL Reconstructive Surgery on Temporal Variations of Cytokine Levels in Synovial Fluid
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) reconstruction restores knee stability but does not reduce the incidence of posttraumatic osteoarthritis induced by inflammatory cytokines. The aim of this research was to longitudinally measure IL-1β, IL-6, IL-8, IL-10, and TNF-α levels in patients subjected to ACL reconstruction using bone-patellar tendon-bone graft. Synovial fluid was collected within 24-72 hours of ACL rupture (acute), 1 month after injury immediately prior to surgery (presurgery), and 1 month thereafter (postsurgery). For comparison, a "control" group consisted of individuals presenting chronic ACL tears. Our results indicate that levels of IL-6, IL-8, and IL-10 vary significantly over time in reconstruction patients. In the acute phase, the levels of these cytokines in reconstruction patients were significantly greater than those in controls. In the presurgery phase, cytokine levels in reconstruction patients were reduced and comparable with those in controls. Finally, cytokine levels increased again with respect to control group in the postsurgery phase. The levels of IL-1β and TNF-α showed no temporal variation. Our data show that the history of an ACL injury, including trauma and reconstruction, has a significant impact on levels of IL-6, IL-8, and IL-10 in synovial fluid but does not affect levels of TNF-α and IL-1β.
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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 | 0.000 | 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 | 0.000 |
| 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".