[Short-term effectiveness of joint distraction by Ilizarov combined with arthroscopic debridement in treatment of knee osteoarthritis].
Bibliographic record
Abstract
Objective: To investigate the short-term effectiveness of joint distraction by Ilizarov combined with arthroscopic debridement in the treatment of knee osteoarthritis (KOA). Methods: Between January 2014 and January 2015, 15 patients (15 knees) with KOA were treated using arthroscopic debridement assisting with the Ilizarov distraction technology. There were 7 males and 8 females, aged from 45 to 64 years (mean, 55 years). The left knee and the right knee were involved in 6 and 9 cases respectively. The disease duration was 2.0-9.5 years (median, 6 years). They all had received conservative treatment for 6 months and got poor clinical improvement. The preoperative visual analogue scale (VAS) score, the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score, the knee injury and osteoarthritis outcome score (KOOS), the range of motion (ROM) for knee, and the radiographic joint space width were 76.2±8.8, 59.3±5.7, 44.3±7.2, (75±21)°, and (2.5±0.4) mm respectively. According to Kellgren-Lawrence grade system, 11 cases were rated as grade III and 4 cases as grade IV. Results: =0.000). Conclusion: Joint distraction by Ilizarov combined with arthroscopic debridement can effectively relieve pain, improve the function and quality of life. It was beneficial to cartilaginous tissue repair and delaying the degenerative process of KOA. The short-term effectiveness is satisfactory.
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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 | 0.000 | 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".