Association between abnormal glycoprotein and diabetic peripheral neuropathy in patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Background and objectives: To investigate the association between serum abnormal glycoprotein (AP) and diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM). Materials and methods: This cross-sectional study enrolled 476 patients with T2DM. DPN was diagnosed using the Toronto Clinical Neuropathy Score (TCNS) and nerve conduction studies. The association between AP and DPN was evaluated using multivariable logistic regression, restricted cubic spline analysis (RCS), and receiver operating characteristic (ROC) curve analysis. Results: Serum AP levels were significantly higher in patients with DPN than in those without (P < 0.001). After adjusting for multiple confounders, elevated AP emerged as an independent risk indicator for DPN (OR = 1.024, 95% CI: 1.012-1.036). A non-linear relationship was observed, with a marked increase in DPN risk when AP levels exceeded an inflection point of 119.628 μm². Combining AP with clinical variables significantly enhanced predictive accuracy for DPN, increasing the area under the curve (AUC) from 0.686 to 0.805. Conclusions: Elevated serum AP represents a novel and independent risk indicator for DPN in patients with T2DM. Its integration into clinical practice may facilitate early detection for DPN.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".