Therapeutic effect evaluation of autologous platelet-rich plasma in the treatment of patellofemoral osteoarthritis based on MOCART 2.0 knee score system
Bibliographic record
Abstract
Objective To explore the therapeutic effect of autologous platelet-rich plasma (PRP) in the treatment of patellofemoral osteoarthritis (PFOA) based on the Magnetic Resonance Observation of Cartilage Repair Tissue 2.0 (MOCART 2.0) knee score system. Methods A total of 22 patients (28 knees) with PFOA treated by intraarticular PRP injection in our hospital from January 2020 to December 2023 were enrolled in the study. The nuclear magnetic resonance(NMR) images before and after treatment were compared using the MOCART 2.0 knee score system. Visual Analog Scales (VAS) score and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score before and after treatment were evaluated and compared, and adverse reactions were observed during the treatment. Results The MOCART 2.0 knee score showed no difference before and after treatment (53.4±12.2 vs 49.9±2.1)(P>0.05). In the subgroup analysis, the "signal intensity of the repair tissue" score and the "subchondral changes" score were better than those before treatment as(5.5±1.5)vs(4.6±1.5)and(15.9±4.3)vs(13.2±6.4)(P<0.05). There were no significant changes in indicators as " degree of cartilage filling" , " integration with adjacent cartilage edges" , " repair of tissue surface structure" , " repair of tissue structure" and " bone changes" (P>0.05).The VAS score and WOMAC score gradually decreased at 1 month, 3 months and 6 months after treatment and were better than before treatment (P<0.05). Incidence of adverse reactions was 4% (4/112). Conclusion Intraarticular PRP injection is an effective and safe option for treatment of PFOA, and can improve the cartilage signal intensity and alleviating subchondral bone edema.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".