The curative efficacy of arthroscopic therapy in treating anterior cruciate ligament rupture with secondary osteoarthritis.
Bibliographic record
Abstract
OBJECTIVE: To compare and analyze the clinical effects of arthroscopic therapy and drug therapy in treating anterior cruciate ligament (ACL) rupture with secondary osteoarthritis (OA). PATIENTS AND METHODS: A total of 68 patients that were diagnosed as ACL rupture with secondary OA in our hospital from February 2014 to February 2015 were enrolled in our study. All of the patients were randomly divided into control group (n = 30) and observation group (n = 38) according to the order of admission. The patients in the control group were given analgesic, anti-inflammatory drugs + functional rehabilitation training whereas the patients in the observation group were given ACL reconstruction + OA debridement and functional rehabilitation training under arthroscopy. RESULTS: The success rate of the observation group was 92.1%. After 3-month follow-up, the clinical total effective rate of the observation group was significantly higher than that of the control group, the prevalence of complications in the observation group was significantly lower than in the control group, and differences were statistically significant (p < 0.05). Lysholm scale scoring of observation group was significantly higher than of the control group, modified McGill pain scale score was significantly lower than that of the control group, and differences were statistically significant (p < 0.05). Quadriceps muscle peak torque, average power, and the optimal single work at 60°/s, 120°/s, and 180°/s were significantly higher than those of the control group, and differences were statistically significant (p < 0.05). CONCLUSIONS: Arthroscopic operative therapy was safe and effective for the treatment of ACL with secondary OA. Compared with drug therapy, it can significantly improve the movement function of the knee joint, so it was worthy of clinical application.
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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".