Value of shear wave elastography combined with the Toronto clinical scoring system in diagnosis of diabetic peripheral neuropathy
Bibliographic record
Abstract
ABSTRACT: To evaluate the diagnostic values of shear wave elastography (SWE) alone and in combination with the Toronto clinical scoring system (TCSS) on diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM).The study included 41 DPN patients, 42 non-DPN patients, and 21 healthy volunteers. Conventional ultrasonography and SWE were performed on the 2 sides of the tibial nerves, and cross-sectional area (CSA) and nerve stiffness were measured. TCSS was applied to all patients. A receiver operating characteristic curve analysis was performed.The stiffness of the tibial nerve, as measured as mean, minimum or maximum elasticity, was significantly higher in patients in the DPN group than the other groups (P < .05). The tibial nerve of subjects in the non-DPN group was significantly stiffer compared to the control group (P < .05). There was no significant difference of the tibial nerve CSA among the 3 groups (P > .05). Mean elasticity of the tibial nerve with a cutoff of 71.3 kPa was the most sensitive (68.3%) and had a higher area under the curve (0.712; 0.602-0.806) among the 3 shear elasticity indices for diagnosing DPN when used alone. When combining SWE with TCSS in diagnosing DPN, the most effective parameter was the EMax, which yielded a sensitivity of 100.00% and a specificity of 95.24%.SWE is a better diagnostic tool for DPN than the conventional ultrasonic parameter CSA, and a higher diagnostic value is attained when combining SWE with TCSS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".