Arthroscopic Debridement Combined with Rehabilitation Training in the Treatment of Knee Osteoarthritis
Bibliographic record
Abstract
Objective: The clinical effect of arthroscopic debridement combined with rehabilitation training in the treatment of knee arthritis was studied and analyzed. Methods: A total of 90 patients with knee osteoarthritis treated in our hospital from August 2017 to August 2018 were selected as the research objects. All patients were divided into the observation group and the control group by using the random number method. The control group was treated by arthroscopic cleaning operation, and the observation group was treated by combined rehabilitation training on the basis of the treatment in the control group. The total effective rate and simple McGill pain of the two groups were compared Score and lyshoim score. Result: The total effective rate of the observation group was significantly higher than that of the control group (P < 0.05), the difference was statistically significant; after treatment, the simple McGill pain score of the observation group was significantly lower than that of the control group (P < 0.05), the difference was statistically significant, the lyshoim score of the observation group was significantly better than that of the control group (P < 0.05), the difference was statistically significant. Conclusion: In the treatment of knee osteoarthritis, arthroscopic debridement combined with rehabilitation training has a significant effect, which can significantly reduce the pain and improve the prognosis of patients. It is worth popularizing in clinical treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".