Effect of mouse nerve growth factor combined with mecobalamine on treatment of diabetic peripheral neuropathy
Bibliographic record
Abstract
Objective: To observe the clinical effect of mouse nerve growth fact (NGF) combined with \nmecobalamine on treatment of diabetic peripheral n-europathy (DPN). Methods: A total of \n84 cases of patients with DPN treated in ourhospital between April 2012 and June 2015 were \nselected, and divided into study group and control group randomly (n=42); Control group \nwas only given mecobalamine treatment, while study group was given mouse nerve growth \nfactor combined with mecobalamine treatment for 4 weeks. TThe motor nerve conduction \nvelocity median nerve (MNCV), sensory nerve conduction velocity (SNCV), serum high \nsensitivity c-reactive protein (hs-CRP) and Toronto clinical scoring system (TCSS) changes \nof median nerve and nervus peroneus communis before and after treatment were compared. \nResults: There were no significant differences in MNCV, SNCV of mediannerve and nervus \nperoneus communis before treatment. MNCV and SNCV of both groups after treatment were \nsignificantly increased. MNCV, SNCV of mediannerve and nervus peroneus communis in \nstudy group was significantly higher than that in control group. hs-CRP and TCSS scoring \nof both groups before treatment showed no statistic significant difference. hs-CRP scoring of \nboth groups after treatment showed no significant difference. TCSS scoring was significantly \nlower than that in control group. Adverse reaction total occurrence rate after given drug in \nstudy group was 16.67% (7/42), compared with 7.14% (3/42) in control group, difference \nwas significant. Conclusions: Mouse NGF combined with mecobalamine could achieve \ngood curative effect. It is of higher safety in the treatment of patients with DPN, and deserves \npopularization and application.
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".