<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 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) | 0.000 | 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".