[A comparison of clinical effectiveness of different neuropathy scoring systems in screening asymptomatic diabetic peripheral neuropathy].
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical effectiveness in screening asymptomatic diabetic peripheral neuropathy (ADPN) by the Michigan neuropathy screening instrument (MNSI) and the Toronto clinical scoring system (TCSS). METHODS: MNSI, TCSS and neural electrophysiological test (NET) were conducted in 232 neurologically asymptomatic type 2 diabetes patients. By using the results of NET as the golden criteria for diagnosis of ADPN, we evaluated the effectiveness of the two different scoring system by the receiver operator characteristic curve. The sensitivity, specificity, positive and negative predictive values, accuracy, Youden indexes and kappa values on different diagnostic cut-off points of MNSI and TCSS were analyzed. The correlation between the two different scoring system and the risk factors of diabetic peripheral neuropathy (DPN) were also analyzed. RESULTS: The area under the ROC curve of MNSI and TCSS were 0.792, 0.704, respectively. The sensitivity, specificity, accuracy, Youden indexes and kappa values of MNSI over 2 and TCSS over 2 were 66.2%vs 73.3%, 90.4% vs 63.7%, 78.3% vs 68.5%, 0.566 vs 0.370, and 0.588 vs 0.345, respectively. MNSI was better than TCSS in the effectiveness of diagnosing ADPN and consistence with the result of NET. Moreover, MNSI was associated with the most related risk factors of DPN including age, glycosylated hemoglobin (HbA1c), HbA1c × disease duration, islet function and HDL-C. CONCLUSIONS: MNSI could be used as a relatively simple and reliable method for clinical and epidemiological screening and assessment of ADPN.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".