Nerve decompression combined with Danhong injection for the treatment of painful diabetic peripheral neuropathy
Bibliographic record
Abstract
Objective The clinical efficacy of microsurgical peripheral nerve decompression combined Danhong injection for treatment of painful diabetic peripheral neuropathy of lower limbs is analyzed. Methods A total of 28 patients with painful diabetic peripheral neuropathy were enrolled in this study. Both the common peroneal nerve,deep peroneal nerve,posterior tibial nerve and its branches were performed the nerve decompression. And then Danhong was administered in the experimental group,while Methylcobalamin was used in the control group. Results In the control group,47. 0% of the patients got 50% pain relief,and 64. 7% of patients got 30% pain relief.However,the rate was 72. 2% and 94. 4% in the experimental group; more than 5 m/ s sensory conduction velocity improvement of tibial nerve and the peroneal nerve were 72. 2% and 83. 3% in the experimental group,compared to47. 1% and 70. 6% in the control group; Toronto clinical scoring system( TCSS) scores of the experimental group( 6.71 ±1.98) were also better than that of the control group( 4. 93 ±2. 50). Conclusion Microsurgical peripheral nerve decompression is effective for the treatment of painful diabetic peripheral neuropathy,and a better curative effect can be achieved if combined with post-operative Danhong injection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".