<p>The impact of changes in serum levels of metalloproteinase-2 and metalloproteinase-9 on pain perception in patients with disc herniation before and after surgery</p>
Bibliographic record
Abstract
Purpose: The aim of our research was to investigate the link between serum levels of metalloproteinase-2 (MMP-2) and MMP-9, and the degree of pain experienced before and 1 and 3 months after microdiscectomy in 70 patients with disc herniation (DH). Patients and methods: The control group (group C) consisted of 70 healthy subjects and the DH group consisted of 70 patients with sciatica pain caused by lumbar DH. Before (DH0) and 1 and 3 months after surgery, the patients were assessed in terms of the following biochemical parameters: MMP-2, tissue inhibitors of metalloproteinases-2 (TIMP-2), MMP-2/TIMP-2, MMP-9, TIMP-1, and MMP-9/TIMP1, and the following clinical parameters: Numeric Rating Scale for the back (NRS-B) and the leg (NRS-L) and the Pain Rating Index (PRI) and Present Pain Intensity (PPI) of the McGill Pain Questionnaire. Results: No statistically significant correlations were observed following the biochemical and clinical assessments performed in group C and the DH group before surgery. After surgery (1 month), higher levels of TIMP-1 correlated with higher levels of NRS-B (rs =0.27; p <0.05). At 3 months after surgery higher levels of TIMP-2 and lower levels of MMP-2/TIMP-2 were correlated with higher levels of NRS-L (rs =0.27, p <0.05 and rs =−0.31, p <0.05, respectively) and higher levels of TIMP-2 were correlated with higher PRI scores (rs =0.27; p <0.005) and PPI scores (rs =0.35; p <0.01). Conclusion: The results showed that MMPs are involved in DH and play a significant role in the perception of pain after DH surgery. However, the value of MMPs as a potential therapeutic target in pain treatment should be considered cautiously. Keywords: neuropathic pain, disc herniation, metalloproteinases, tissue inhibitors of metalloproteinases
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".