Assessing the effectiveness of Toronto clinical neuropathy score in diagnosing diabetic peripheral neuropathy – A descriptive comparative study
Bibliographic record
Abstract
Background: There has been an increasing prevalence of diabetes worldwide. Diabetic peripheral neuropathy is the most common complication of type I and II diabetes. Peripheral neuropathy is a damage occurring in the nerves due to prolonged higher levels of blood sugar and diabetes. There are various screening tests such as vibration perception threshold (VPT), neuropathy symptom profile, michigan neuropathy screening instrument, neuropathy disability score, and Michigan diabetic neuropathy score. VPT accounts for an easy and accurate identification of diabetic patients, who are at risk, including patients, with early neuropathic deficits. Toronto clinical neuropathy score (TCNS) has been validated as a score for monitoring and diagnosing diabetic peripheral neuropathy. Aims and Objectives: This study was done to estimate the sensitivity, evaluate specificity, and determine the positive predictive value and negative predictive value of TCNS scoring in diabetic peripheral neuropathy patients considering VPT as the gold standard. Materials and Methods: In this study, VPT and TCNS were determined in 100 type 2 diabetic subjects with signs and symptoms of peripheral neuropathy. Results: In this study, it was estimated that TCNS is a reliable measure of distal poly neuropathy with a sensitivity of 95.83%, 100% specificity, positive predictive value of 100%, and negative predictive value of 50%. Conclusion: From our study, it was found that TCNS is a sensitive indicator of definite clinical neuropathy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".