Translation, validation, and diagnostic accuracy of the Arabic version of the Michigan neuropathy screening instrument
Bibliographic record
Abstract
ABSTRACT: The Michigan Neuropathy Screening Instrument (MNSI) is used to screen patients for diabetic neuropathy (DNP). We aimed to translate the MNSI questionnaire into Arabic (MNSIq-Ar) and to assess the validity and diagnostic performance of the MNSI Arabic version (MNSI-Ar).Cronbach alpha α and the interclass correlation coefficient were used to measure the reliability and reproducibility of the MNSIq-Ar. The instrument's validity was assessed by Spearman correlation with the Utah Early Neuropathy Scale (UENS), the Modified Toronto Neuropathy Score (mTCNS), diabetic neuropathy symptoms (DNS), and sural nerve amplitude (SNA). The construct validity of the MNSI-Ar was assessed by its ability to differentiate the severity of DNP (using the Kruskal-Wallis test). The diagnostic performance was assessed through the receiver operator curve area.We recruited 89 participants (mean [SD] age, 50.8 [12.3] years; 48% men). The MNSIq-Ar showed an α of 0.81 and intraclass correlation coefficient = 0.94, and the correlation coefficients with UENS, mTCNS, DNS, and sural nerve amplitude were 0.67, 0.83, 0.73, and -0.49, respectively (all P < .0001). The MNSI-Ar was able to differentiate the different severities of DNP. The receiver operator curve area was 0.93 with a high sensitivity of 95.9% and 100% for probable and confirmed DNP, respectively.MNSI-Ar is a reliable and valid tool to screen for diabetic neuropathy in the Arabic language with a good diagnostic performance and high sensitivity.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".