Assessing diabetic polyneuropathy in Spanish‐speaking patients: Translation and validation of the Toronto Clinical Neuropathy Score
Bibliographic record
Abstract
BACKGROUND AND AIMS: Diabetic sensorimotor polyneuropathy (DSP) is a common complication of diabetes. The Toronto Clinical Neuropathy Score (TCNS) is a useful tool for detecting DSP. However, it is not available in Spanish. The study aimed to translate and culturally adapt the TCNS and modified (mTCNS) scales into Spanish and evaluate their measurement properties. METHODS: A multistep forward-backward method was used for translation and cultural adaptation. A panel of physicians subjected the final Spanish versions of TCNS and mTCNS (TCÑS, mTCÑS) to cognitive debriefing. Consecutive patients with diabetes mellitus and DSP were recruited from an outpatient clinic, and the TCÑS and mTCÑS were tested for construct validity, along with other measures. RESULTS: The internal consistency of both TCÑS and mTCÑS was excellent, as evidenced by Cronbach's Alpha coefficients of 0.83 and 0.85, respectively. Furthermore, there was a robust positive correlation between TCÑS and mTCÑS. In addition, TCÑS was found to exhibit a strong negative correlation with sural sensory nerve action potential amplitude (r = -0.9206) and peroneal compound motor action potential amplitude (r = -0.729), while demonstrating a positive and strong correlation with the Michigan Neuropathy Screening Instrument (r = 0.713). INTERPRETATION: The TCÑS and mTCÑS are reliable and valid translations of the original TCNS. The TCÑS and mTCÑS can be used to diagnose and measure the severity of neuropathy in Spanish-speaking patients with diabetes.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".