Applicability of Toronto Clinical Neuropathy Scoring and its Correlation with Diabetic Peripheral Neuropathy: A Prospective Cross-sectional Study
Bibliographic record
Abstract
Introduction: Diabetes is a non-communicable metabolic disorder which is associated with numerous vascular and non-vascular complications. Neuropathy is one of the most important complications which, if not recognized and treated early may result in significant disability and poor quality of life. In a resource poor setting like India, where diagnostic modalities like Nerve Conduction Study (NCS) are expensive for early diagnosis, the present study aimed to evaluate the effectiveness of a simple bed side assessment test, the Toronto Clinical Neuropathy Scoring (TCNS) system in diagnosing Diabetic Peripheral Neuropathy (DPN). Aim: The primary objective was to determine the applicability of Toronto clinical scoring system in DPN diagnosed by NCS in the South Indian population. The secondary objective was to evaluate the correlation between duration of Diabetes Mellitus (DM), HbA1C, diabetic retinopathy and neuropathy with severity of diabetic neuropathy as determined by the TCNS. Materials and Methods: In a prospective cross-sectional study, conducted over a period of 12 months from June 2015 to May 2016 at a tertiary care institute in semi-urban South India, 50 diabetic patients with symptomatic neuropathy were included. All patients were subjected to TCNS and the results were compared with neuropathy confirmed by NCS. Categorical variables were expressed as percentage or proportions. Comparison of normally and abnormally distributed continuous variables were done by independent sample t-test and Mann – Whitney U test respectively. Categorical variables were compared using Chisquare test or Fisher’s exact test. A p-value less than 0.05 was considered statistically significant. Results: The presence of neuropathy by TCNS was confirmed in all cases by NCS. Further the severity of neuropathy as assessed by TCNS was found to correlate well with duration of diabetes, and the presence of diabetic retinopathy and nephropathy. Presence of foot weakness, ataxia and upper limb symptoms also had direct correlation with severity of diabetic neuropathy. Conclusion: TCNS is a sensitive scoring system used to diagnose diabetic neuropathy and can be used as an inexpensive bedside screening tool.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".