Effect of resistance exercise on peripheral neuropathy in Type 2 diabetes mellitus.
Bibliographic record
Abstract
OBJECTIVES: To explore the improvement of neurological symptoms in patients with Type 2 diabetic peripheral neuropathy via resistance exercise. METHODS: =50). Resistance exercise was performed on the bioDensity™ resistance exercise instrument. The study graded the severity of diabetic peripheral neuropathy by the Toronto clinical scoring system (TCSS), and the improvement of diabetic peripheral neuropathy (DPN) was evaluated by the decline of the TCSS score. The observation group was treated with resistance exercise for 6 months. The changes of body mass index (BMI), waist circumference, hip circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose (FBG), fasting insulin (FINS), glycosylated hemoglobin (HbA1C), total cholesterol (TC), glycerin trilaurate (TG), low density lipoprotein (LDL), high density lipoprotein (HDL), and TCSS score were compared between baseline and 3, 6 months of exercise. At the same time, the differences in sensory test scores, nerve reflex scores, and neurological symptom scores were compared between the baseline, 3 and 6 months, in the observation group. Except for resistance exercise, the other treatments in the control group were the same as those in the observation group. RESULTS: >0.05). CONCLUSIONS: After the intervention of resistance exercise, the blood glucose and DPN can be improved in a certain extent, and which can be popularized in Type 2 diabetes patients.
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) | 0.002 | 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".