Predictive value of biliverdin reductase‐A and homeostasis model assessment of insulin resistance on mild cognitive impairment in patients with type 2 diabetes
Bibliographic record
Abstract
AIMS/INTRODUCTION: To investigate the predictive value of the biliverdin reductase-A (BVR-A) and the homeostasis model assessment for insulin resistance (HOMA-IR) on mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus, and to establish a nomogram model. MATERIALS AND METHODS: This study included 140 patients with type 2 diabetes mellitus. Based on Montreal Cognitive Assessment (MoCA) scores, participants were categorized into the normal cognitive function (T2DM-NCF) group (65 cases) and the mild cognitive impairment (T2DM-MCI) group (75 cases). Multivariate logistic regression analysis was performed to identify the factors associated with MCI in patients with type 2 diabetes mellitus. A nomogram prediction model was developed using R software for the selected factors, and its predictability and accuracy were verified. RESULTS: Compared with the T2DM-NCF group, subjects with MCI were older, had a longer duration of diabetes, higher HOMA-IR, lower BVR-A, lower cognitive scores, and lower education levels (all P < 0.05). Multivariate logistic regression analysis showed that duration of diabetes (OR = 1.407, 95% CI: 1.163-1.701), HOMA-IR (OR = 1.741, 95% CI: 1.197-2.53), and BVR-A (OR = 0.528, 95% CI: 0.392-0.712) were significantly associated with the development of MCI in patients with type 2 diabetes mellitus. The C-index of the nomogram was 0.863 (95% CI: 0.752-0.937). CONCLUSIONS: Our findings suggest that BVR-A and HOMA-IR are significantly associated with the development of MCI in patients with type 2 diabetes mellitus. The nomogram incorporating BVR-A and HOMA-IR aids in predicting the risk of developing MCI in these patients.
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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 | 0.004 |
| 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 | 0.000 |
| 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".