Neuropathy scale score as an independent risk factor for myocardial infarction in patients with type 2 diabetes
Bibliographic record
Abstract
Abstract Aims To investigate whether peripheral neuropathy scale scores are associated with myocardial infarction (MI) in patients with type 2 diabetes mellitus (T2DM). Materials and Methods In this cross‐sectional study, 32,463 T2DM patients were enroled from 103 tertiary hospitals in 25 Chinese provinces. Based on a history of MI, participants were divided into the MI group ( n = 4170) and the non‐MI group ( n = 28,293). All patients were assessed using four neuropathy scales, namely, Neurological Symptom Score (NSS), Neurological Disability Score (NDS), Toronto Clinical Scoring System (TCSS), and Michigan Neuropathy Screening Instrument (MNSI), and some of the patients underwent evaluation of nerve conduction velocity (NCV) ( n = 20,288). The relationship between these scores and myocardial infraction was analysed. Results The neuropathy scale scores in the MI group were higher than those in the non‐MI group ( p < 0.001). After dividing patients into four groups based on the grading criteria, our results showed that, in addition to aggravating the degree of neuropathy signs, the incidence of MI increased ( p < 0.001). Logistic regression analysis results showed that neuropathy scale scores and NCV were both independent risk factors for MI ( p < 0.001). Furthermore, among the scales used, MNSI presented a higher odds ratio and area under the curve (AUC; 0.625, p < 0.001) than the other three scales (AUC NSS = 0.575, AUC NDS = 0.606, and AUC TCSS = 0.602, p < 0.001) for MI. Conclusions Increased scores on these neuropathy scales (NSS, NDS, TCSS, and MNSI) and NCV were significantly associated with increased risk of MI and were considered independent risk factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".