Standing Balance Performance and Knee Extensors’ Strength in Diabetic Patients with Neuropathy
Bibliographic record
Abstract
Diabetes peripheral neuropathy (DPN) leads to balance impairment among diabetes mellitus (DM). The aims of this study were to (1) distinguish between DM patients who have/do not have DPN and to (2) compare quadriceps' strength and balance performance of DM, DPN, and healthy groups. Fifteen healthy females and 33 females with type 2 diabetic patients participated. The electrodiagnostic method was used to classify diabetic patients into DM and DPN. A dynamometer was used to measure quadriceps' strength. Single-leg standing on a force plate was also used to quantify participants' balance. Smaller conduction velocity and amplitude and greater distal latency of all nerves were observed in the DPN compared with the DM in particular for sensory nerve. In DPN, conduction velocity was asymmetrical. The quadriceps' strength of both legs in DPN and the right leg in DM was smaller than in the control group. The root mean square of the center of pressure was similar between DM and DPN. But it was larger in DPN than in the control group. DPN is associated with asymmetrical conduction velocity, smaller quadriceps' strength, and weaker balance performance that is suggestive of higher risk of falling. Balance training is recommended for the DPN group during their rehabilitation to reduce their falling risk.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".