Optimal cut‐off value of the modified Toronto Clinical Neuropathy Score in the diagnosis of polyneuropathy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The modified Toronto Clinical Neuropathy Score (mTCNS) is a valid and reliable scale for the diagnosis and staging of diabetic sensorimotor polyneuropathy (DSP). The aim of this study was to determine the optimal diagnostic cut-off value of the mTCNS in diverse polyneuropathies (PNPs). METHODS: Demographics and mTCNS values were retrospectively extracted from an electronic database of 190 patients with PNP and 20 normal controls. Sensitivity, specificity, and likelihood ratios and area under the receiver-operating characteristic (ROC) curve were determined for each diagnosis and different cut-off values of the mTCNS. Patients underwent clinical, electrophysiological and functional assessments of their PNP. RESULTS: Forty-three percent of PNP was related to diabetes or impaired glucose tolerance. mTCNS was significantly higher in patients with PNP than in those without (15.27 ± 8 vs. 0.79 ± 1.4; p = 0.001). The cut-off value for diagnosing PNP was ≥3 (sensitivity 98.4%, specificity 85.7%, positive likelihood ratio 6.88). The area under the ROC curve was 0.987. CONCLUSION: A value of 3 or more on the mTCNS is recommended for the diagnosis of PNP.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".