Relationship between ultrasonographic nerve morphology and severity of diabetic sensorimotor polyneuropathy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In the current study, the aim was to characterize the nerve ultrasound cross-sectional areas (CSAs) of type 2 diabetic patients with diabetic sensorimotor polyneuropathy (DSP) of different severities. METHODS: A hundred symptomatic DSP patients and 40 age-matched healthy controls were prospectively recruited. DSP severity was ascertained through the Toronto Clinical Scoring System (TCCS). Nerve electrophysiology and ultrasound were performed on both lower limbs and the non-dominant upper limb. RESULTS: The sural nerve was inexcitable in 19.1% of mild, 40.0% of moderate and 69.0% of severe DSP groups. In contrast, CSAs were measurable in all nerves of DSP patients and were significantly larger compared to controls. Patients with severe DSP had significantly larger ulnar, peroneal, tibial and sural nerves compared to mild DSP patients. By receiver operating characteristic curve analysis, the cut-off value for the sural nerve at 2 mm(2) was a good discriminator (area under the curve 0.88) between the presence and absence of DSP (sensitivity 0.90; specificity 0.74) but performed less well in discriminating between the severity of DSP (cut-off 2.75 mm(2); area under the curve 0.62; sensitivity 0.59; specificity 0.73). Significant correlations were demonstrated between TCSS scores, most neurophysiology parameters and CSAs of the ulnar, peroneal, tibial and sural nerves. CONCLUSION: Nerve ultrasound in DSP reveals enlarged CSAs and these changes worsen with increasing disease severity, thus serving as a useful diagnostic tool especially when neurophysiology is unrevealing.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it