Correlation between Severity of Diabetic Neuropathy and Somatosensory Evoked Potentials Study
Bibliographic record
Abstract
Objective: To investigate the clinical applicability of the somatosensory evoked potentials (SEPs) study in early detection of diabetic neuropathy, and compare the results in different degrees of the disease. Method: The study was performed retrospectively with prospective data collection. The Toronto clinical scoring system was taken as well as nerve conduction study, needle electromyography, and SEPs study with median and posterior tibial nerve stimulations in thirty-eight diabetic patients and twenty non-diabetic adults. The subjects were divided into the non-neuropathy group and the neuropathy group, and the latter was divided into three subgroups (suspected, probable, and definite) according to the degree of neuropathy. Statistical analysis was performed with height and age-related correction of reference values of the latency of SEPs with posterior tibial nerve stimulation. Results: The Toronto clinical scoring system showed concordance with the degree of the diabetic neuropathy (p＜0.05, correlation coefficient=0.827). SEPs study with posterior tibial nerve stimulations showed statistically significant latency delay, not only in the neuropathy group, but also in the non-neuropathy group, compared with the non-diabetic group (p＜0.05). Moreover, the latency delay was noted in proportion to the degree of the diabetic neuropathy within the neuropathy group. Interpretation of the data with height and age-corrected reference values of latency of posterior tibial SEPs had stronger correlation. Conclusion: The SEPs study is useful in the early diagnosis of diabetic neuropathy. However, application of the SEPs to clinical use needs to go through height and age correction. (J Korean Acad Rehab Med 2008; 32: 73-79)
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 | 0.004 |
| 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) | 0.002 | 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".