Comparison of effectiveness among five screening tests for diabetic peripheral neuropathy
Bibliographic record
Abstract
Objective Five simple screening tests, including Toronto clinical scoring system(TCSS), Michigan neuropathy screening instrument(MNSI), Diabetic neuropathy symptom score(DNS) , vibration testing by a 128-Hz tuning fork,and 10g semmes-weinstein monofilament examination(SWME), were analyzed in T2DM to evaluate the effectiveness for diabetic peripheral neuropathy(DPN). Methods 419 patients with type 2 diabetes mellitus underwent the measurements of DPN with TCSS,MNSI,DNS,128-Hz tuning fork and 10g-SWME. DPN was diagnosed by neurological examination,motor and sensory nerve conduction velocity,vibration perception threshold,and warm and cold thermal perception threshold. The effectiveness of the five tests was assessed by using ROC curve analysis. Results There were significant differences in age,duration of diabetes , duration of symptoms for diabetic neuropathy between two groups(all P value 0.001).All five screening tests were significantly associated with NCV,warm and cold thermal perception threshold and vibration perception threshold(all P value 0.001).TCSS showed the best correlation with NCV and thermal perception threshold. Area under the ROC curve values for TCSS,MNSI,DNS,tuning fork and 10g-SWME test were 0.855,0.679,0.669,0.716,0.599,respectively.The optimal cut-points of them provided sensitivity of 79.9%,51.1%,62.3%,43.7%,20.5%, and specificity of 77.5%,79.5%,67.5%,99.3%,99.3%, respectively. Conclusions For screening DPN, simple tool tests are not always reliable and the results from general clinical examination may be a good way.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".