DIAGNOSTIC ACCURACY OF CLINICAL SCORING SYSTEMS (NSS, NDS, AND TCSS) COMPARED TO ELECTROPHYSIOLOGICAL TESTING IN DIABETIC NEUROPATHY AMONG T2DM PATIENTS
Bibliographic record
Abstract
Diabetic neuropathy is one of the most common chronic complications in diabetes mellitus patients and can significantly impair quality of life. Early diagnosis is essential, but limited availability of diagnostic tools such as electrophysiological testing in many healthcare facilities calls for more practical and efficient alternatives. This study aimed to compare the diagnostic accuracy of three clinical scoring systems—NSS (Neuropathy Symptom Score), NDS (Neuropathy Disability Score), and TCSS (Toronto Clinical Scoring System)—in detecting diabetic neuropathy among Type 2 DM patients at Dr. Mohammad Hoesin Hospital, Palembang. A cross-sectional design was employed, and findings revealed that TCSS had the highest accuracy (90.3%) compared to NDS (85.5%) and NSS (85.4%). TCSS also demonstrated the best balance of sensitivity (95.4%) and specificity (77.8%). All three instruments can serve as effective early screening tools, especially in healthcare settings with limited access to electrophysiological diagnostic facilities.
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How this classification was reachedexpand
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".