Clinical efficacy of platelet-rich plasma combined with arthroscopic meniscal plasty on pain, function and physiologic indicators in elderly patients with knee meniscus injury: a retrospective observational study.
Bibliographic record
Abstract
OBJECTIVE: To explore the clinical efficacy of platelet-rich plasma (PRP) combined with arthroscopic meniscal plasty on meniscus injury of the knee joint in the elderly. METHODS: Fifty-six elderly patients with meniscus injuries were evaluated, including 28 patients who underwent arthroscopic meniscal repair and 28 patients who underwent arthroscopic meniscus repair combined with PRP injection. Primary outcomes included visual analogue scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Lysholm score, Lequesne index, Range of motion (ROM), and secondary outcomes included bone gla-protein (BGP), insulin-like growth factor-1 (IGF-1), and matrix metalloproteinase-1 (MMP-1). The primary and secondary measurement outcomes were assessed for each patient before and after the 12 weeks of treatment. RESULTS: The VAS, WOMAC, Lysholm, Lequesne, and ROM were more improved in the PRP group compared to the control group (all P < 0.05). BGP, IGF-1, and MMP-1 were more reduced in the PRP group compared to the control group (all P < 0.05). CONCLUSION: The treatments of PRP combined with arthroscopic meniscal plasty can significantly improve the pain, function, and physiologicindicators in elderly patients.
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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 | 0.002 |
| 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 | 0.001 | 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".