Treatment of horizontal dissection of the knee menisci with platelet-rich plasma (PRP). Literature review and analysis of own data
Bibliographic record
Abstract
BACKGROUND: Treatment of damage to the inner layer of the meniscus of the knee joint that does not extend to the articular surface remains an open question. Subsequently, these injuries can cause a complete rupture of the meniscus that already requires surgical treatment. Existing methods of treatment at this stage of meniscus injury have not shown their effectiveness. AIM: Study the effect of platelet-rich plasma (PRP) on meniscus regeneration. MATERIALS AND METHODS: The analysis of the 15 patients treatment results with the PRP method, which effectively stimulates regenerative processes, was carried out. The effectiveness of the method was assessed using the following evaluation scales: visual analog scale (VAS), Lequesne scale, WOMAC index (Western Ontario and McMaster Universities Osteoarthritis Index), Lysholm scale, KSS scale (Knee Society Score) and magnetic resonance imaging (MRI). RESULTS: According to the results of MRI performed after 6 months, there was no progression of meniscus damage after PRP therapy by all parameters. CONCLUSION: The study showed an improvement in all rating scales. In addition, according to MRI data, after 6 months there was no progression of the degenerative process in the menisci. The presented method can be the first step in the treatment of this pathology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".