Evaluation of the clinical efficacy of multiple lower-extremity nerve decompression in diabetic peripheral neuropathy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the value of high-resolution ultrasonography and neural electrophysiology in early diagnosis, operative opportunity selection, and clinical effect assessment of DPN. METHODS: Five hundred and sixty patients with diabetic peripheral neuropathy (DPN) were treated with DELLON surgical nerve decompression in our hospital in the past 5 years. Before and after 18 months surgery, the tests of the Toronto clinical scoring system, high-resolution ultrasonography, QST, and Nerve Conduction Velocity (NCV) were evaluated in all cases. The control group included 40 diabetic patients in the same age range but without DPN. RESULTS: Ultrasonographic images revealed an apparently normal proximal common peroneal nerve, tibial nerve marked swelling, enlarged, and hypoechogeneity with loss fascicular pattern. The cross-sectional-area, anteroposterior and transverse diameter were measured preoperative and prooperative, and the differences had statistical significance (P < 0.01). NCV-positive cases amount to 74.9% DPN patients in this study and QST-positive cases amount to 90.9% and had significant differences between them (P < 0.05). Postoperative NCV and cold perception threshold significantly increased (P < 0.05) compared with that of the before surgery. Postoperative warm perception threshold (P < 0.01) and vibration perception threshold (P < 0.05) were significantly lower than the preoperative value. NCV was positively correlated with cold perception threshold (r = 0.395, P < 0.01), and negatively correlated with warm perception threshold (r = - 0.387, P < 0.01) and vibratory perception threshold (r = - 0.367, P < 0.01). The preoperative TCSS score was 19 points for all the cases, and 420 cases (75%) improved to 10-13 points (P < 0.01). CONCLUSION: Ultrasonography is capable of depicting these nerves morphological information, with respect to exact location, course, and extent. QST is suitable for early diagnosis of DPN, and abnormal QST is an indication of lower extremity nerve decompression for DPN. The joint use of QST and NCV testing helps surgeons to grasp the timing of surgery. High-resolution ultrasound, QST and NCV testing can also be used as an outcome index for surgical treatment.
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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 | 0.002 |
| 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".