Quantitative Evaluation of Meniscal Healing Process of Degenerative Meniscus Lesions Treated with Hyaluronic Acid: A Clinical and MRI Study
Bibliographic record
Abstract
PURPOSE: We aimed to evaluate clinical efficacy and healing effects of conservative management of degenerative meniscus lesions (DMLs) with a hyaluronic acid (HA) hydrogel. METHODS: Patients were subjected to two HA injections two weeks apart. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Patient's Global Assessment (PtGA) and Clinical Observer Global Assessment (CoGA) of the disease were assessed at baseline, 30, and 60 days after treatment. Short Form (36) Health Survey (SF-36) was assessed at baseline and 60 days after treatment. One year after treatment, patients were called to know whether any of them had undergone arthroscopic partial meniscectomy (APM). All patients underwent magnetic resonance imaging using a 1.5-T Magnetic Resonance Imaging (MRI) scanner (Siemens Aera), which included a T2 mapping pulse sequence with multiple echoes at baseline and 60 days after treatment. RESULTS: 40 patients were enrolled. WOMAC score, physical function subscale, PtGA and CoGA, and SF-36 showed a statistically significant difference between baseline and follow-up. One year after treatment, only one patient had undergone APM. A decrease in the T2 measurement was detected in the posterior horn medial meniscus in 39% of cases in both the red and red-white zone, and in 60% of cases in the white zone; in the posterior horn lateral meniscus in 55% of cases in both the red and white zones, and in 65% of cases in the red-white zone. Only for the latter, there was a statistically significant difference between baseline and posttreatment T2 measurements. CONCLUSION: This study supports the use of HA in the conservative management of DML as it is clinically effective and enhances meniscus healing as demonstrated by T2 measurements. Moreover, it reduces the need for APM at 1-year follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".