Posterior Tibial Nerve Ultrasound Assessment of Peripheral Neuropathy in Adults with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Diabetic peripheral neuropathy (DPN) is a common and debilitating complication of type 2 diabetes mellitus (T2DM). Early detection and prompt institution of appropriate therapy could prevent undesirable outcomes such as paresthesia, pain, and amputation. Although the gold standard for diagnosing DPN is nerve conduction studies, high-resolution peripheral nerve ultrasonography may serve as a noninvasive and low-cost alternative for diagnosing and staging DPN. This study investigated the clinical utility of sonographic posterior tibial nerve cross-sectional area (PTN CSA) for diagnosing DPN in individuals with T2DM. Methods: Eighty consecutive adults with T2DM and 80 age-/sex-matched controls were recruited. Clinical information was obtained, including symptoms, disease duration, Toronto clinical neuropathy score (TCNS), and biochemical parameters. The left PTN CSA at 1 cm, 3 cm, and 5 cm above the medial malleolus (MM) was measured with a high-frequency ultrasound transducer and compared to the detection of DPN using the TCNS. Results: Based on the TCNS, 58 (72.5%) of the T2DM group had DPN. Of these, 14 (24.1%), 16 (27.6%), and 28 (48.3%) participants had mild, moderate, and severe DPN, respectively. All the mean PTN CSA (aggregate, 1 cm, 3 cm, and 5 cm above MM) of the participants with T2DM and DPN (T2DM-DPN) were significantly higher than those of T2DM without DPN (WDPN) and controls. All the PTN CSA increased significantly with increasing severity of DPN. The PTN CSA at 3 and 5 cm levels correlated weakly but significantly with fasting plasma glucose and glycated hemoglobin levels. Conclusion: The PTN CSA is significantly larger in T2DM-DPN than in T2DM-WDPN and healthy controls. PTN ultrasonography can be an additional tool for screening DPN in patients with T2DM.
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 | 0.001 | 0.002 |
| 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".