Reliability and validity of the modified Toronto Clinical Neuropathy Score in diabetic sensorimotor polyneuropathy
Bibliographic record
Abstract
INTRODUCTION: A reliable and valid clinical tool to capture symptoms and signs of diabetic sensorimotor polyneuropathy (DSP) for use in clinical research trials is urgently needed. The validated Toronto Clinical Neuropathy Score (TCNS) was modified to improve sensitivity to early DSP changes. We aimed to assess the reproducibility of this modified tool, the mTCNS and to determine its validity relative to the precursor TCNS. METHODS: Sixty-five patients (six Type 1, 59 Type 2 diabetes) with diabetes duration 13 +/- 8 years were accrued from four study sites and examined on 2 days for internal consistency and inter- and intra-rater reliability of the mTCNS. In the absence of a single quantitative gold-standard measure for DSP, results of the mTCNS were compared with the precursor TCNS for the purpose of estimating validity. RESULTS: Internal consistency of the two domains within the mTCNS was good (Cronbach's alpha 0.78). Very good inter-rater reliability for the mTCNS was demonstrated by an intra-class correlation coefficient for the mTCNS of 0.87 (95% confidence interval, 0.79-0.91), which was similar in magnitude to that of the TCNS (0.83; 95% confidence interval, 0.75-0.89). Intra-rater reliability testing of the mTCNS showed moderate to good correlation for individual symptoms and sensory tests (Cohen's kappa values of 0.54-0.73). The mTCNS shared moderate correlation with the precursor TCNS (Pearson correlation coefficient, 0.58). DISCUSSION: The mTCNS, a clinical score with higher face validity for tracking mild to moderate DSP, has sufficient reliability and validity relative to its precursor TCNS for use in clinical research.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".