Fixation of platelet-rich plasma and fibrin gels on knee cartilage defects after microfracture with arthroscopy
Bibliographic record
Abstract
Abstract Purpose An investigation of arthroscopic surgery combined with coverage of the microfractured wound surface with platelet-rich plasma (PRP) and fibrin gels (FG) to treat knee cartilage defects. Methods Between February 2017 and February 2020, 145 patients with knee cartilage defects were treated. Only isolated full-thickness cartilage defects were included, and 28 patients (12 men and 16 women) were included in this study. They were all treated with arthroscopic surgery on subchondral bones, filled with PRP and thrombin, and sealed with FG. The knee pain visual analogue scale (VAS) scores were measured after the patients climbed ten stairs up and down, and the Western Ontario and McMaster Universities osteoarthritis index and the area of cartilage defects were measured through the pre-operative and post-operative follow-up. The complication incidences were also observed. Results All patients were followed up for ten to 15 months (median 12 months). The knee pain VAS scores decreased from 6.57 ± 1.07 pre-operatively to 2.09 ± 1.35 at the last follow-up. The WOMAC osteoarthritis index decreased from 44.32 ± 3.95 (mean ± sd) pre-operatively to 16.57 ± 2.20 by the last follow-up. The cartilage defect decreased from 2.93 ± 0.65 cm2 pre-operatively to 1.09 ± 0.69 cm2 at the last follow-up. All scores showed statistically significant improvements after surgery (p < 0.05). No complications were observed. Conclusion The combination therapy of arthroscopic surgery and covering the microfractured wound surface with PRP and FG can repair knee cartilage defects, relieve pain, and improve function, and is a safe and effective 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 | 0.000 | 0.000 |
| 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".