Improving Effects of Peripheral Nerve Decompression Microsurgery of Lower Limbs in Patients with Diabetic Peripheral Neuropathy
Bibliographic record
Abstract
Background: Peripheral nerve decompression microsurgery can relieve nerve entrapment and improve the symptoms of DPN. However, postoperative tissue adhesion will produce new pressure on the nerves, affecting the surgical efficacy. In this study, a nerve conduit was used in the peripheral nerve decompression microsurgery to prevent postoperative adhesions, and the role of the nerve conduit in surgical nerve decompression was explored. Methods: A total of 69 patients with DPN were recruited and randomly divided into three groups: the nerve conduit group, conventional surgery group, and control group. Two weeks before surgery and 6 months after surgery, patients in each group were clinically tested using the visual analog scale (VAS) score, neurophysiological test, Toronto clinical scoring system (TCSS) score, and two-point discrimination (2-PD) test. Results: The patients’ symptoms in the nerve conduit group were relieved to varying degrees, and the relief rate reached 90.9%; the treatment efficacy was higher than that in the other groups. The postoperative nerve conduction velocity (NCV) in the two surgical groups was significantly higher than that before the surgery, and the difference between the nerve conduit group and the conventional surgery group was statistically significant (p < 0.05). For the 2-PD test, there was a statistically significant difference between the two surgical groups (p < 0.05). The TCSS score in the two surgical groups was significantly higher than that in the control group (p < 0.01). There was a significant difference in the TCSS scores between the nerve conduit group and the conventional surgery group (p < 0.05). Conclusions: The nerve conduit could further improve the efficacy of peripheral nerve decompression microsurgery in the treatment of DPN.
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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 | 0.000 |
| 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".