Morphological and Mechanical Properties of Lower-Limb Muscles in Type 2 Diabetes: New Potential Imaging Indicators for Monitoring the Progress of DPN
Bibliographic record
Abstract
The aim of this study was to explore changes in morphological and mechanical properties of lower-limb skeletal muscles in patients with diabetes with and without diabetic peripheral neuropathy (DPN) and seek to find a potential image indicator for monitoring the progress of DPN in patients with type 2 diabetes mellitus (T2DM). A total of 203 patients with T2DM, with and without DPN, were included in this study. Ultrasonography and ultrasound shear wave imaging (USWI) of the abductor hallux (AbH), tibialis anterior (TA), and peroneal longus (PER) muscles were performed for each subject, and the shear wave velocity (SWV) and cross-sectional area (CSA) of each AbH, TA, and PER were measured. The clinical factors influencing AbH_CSA and AbH_SWV were analyzed, and the risk factors for DPN complications were investigated. AbH_CSA and AbH_SWV in the T2DM group with DPN decreased significantly (P < 0.05), but no significant differences were found in the SWV and CSA of the TA and PER between the two groups. Toronto Clinical Scoring System (CSS) score and glycosylated hemoglobin (HbA1c) were independent predictors of AbH_CSA and AbH_SWV. As AbH_SWV and AbH_CSA decreased, Toronto CSS score and HbA1c increased and incidence of DPN increased significantly. In conclusion, the AbH muscle of T2DM patients with DPN became smaller and softer, while its morphological and mechanical properties were associated with the clinical indicators related to the progression of DPN. Thus, they could be potential imaging indicators for monitoring the progress of DPN in T2DM patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".