Comparison study of different scoring systems on screening peripheral neuropathy of diabetic patients
Bibliographic record
Abstract
Objective To evaluate the effectiveness of the Michigan neuropathy screening instrument (MNSI),the Michigan Diabetic Neuropathy Score(MDNS) and the Toronto Clinical Scoring System(TCSS) in screening diabetic peripheral neuropathy (DPN) in order to find a rapid,simple and accurate way for DPN screening.Methods Three hundred and twenty-seven type 2 diabetic patients were enrolled in the study.All patients received the 4 simple tests including MNSI,MDNS,TCSS and neural electrophysiological test (NET).Taking the results of NET as the golden criteria,the sensitivity,specificity,positive and negative predictive values,accuracy,Youden indexes and Kappa values of the scoring systems were analyzed to evaluate their clinical effectiveness.Results Compared with NET examination,sensitivity,specificity and accuracy of MDNS were 90.5％ (162/179),68.92％ (102/148),80.73％ (264/327),and 87.71％ (157/179),78.38％ (116/148),83.49％ (273/327) for MNSI and 79.33％ (142/179),65.54％ (97/148),73.09％ (239/327) for TCSS.MNSI and MDNS were better than TCSS in terms of effectiveness of DPN diagnosis and consistence with the result of NET (MDNS:0.6 045 ; MNSI:0.6 648 ; TCSS:0.4524).The area under the ROC curve of MNSI,MDNS and TCSS were 0.757,0.719,0.667.Conclusion Among the three scales methods,MNSI is a better method in screening DPN for its simplicity and reliability. Key words: Diabetes mellitus; Peripheral neuropathy; Screening; Scoring systems
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".