Objective:

To investigate the correlation between the platelet-to-albumin ratio (PAR) and the pathological severity of diabetic nephropathy (DN) while developing a fine-tuning-free large language model framework for risk stratification.

Key Findings:

• The optimal cutoff for PAR was found to be 7.155 with an AUC of 0.716, indicating moderate diagnostic ability.
• A positive correlation between PAR levels and the pathological severity of DN was established (OR: 6.65, 95% CI: 2.617–16.9).
• The fusion LLM framework achieved higher specificity (56.67%) compared to the random forest model (31.67%), highlighting its potential advantages.
• The model's macro-F1 score for assessing interstitial fibrosis and tubular atrophy (IFTA) was 51.00 ± 5.71%, surpassing the XGBoost model's score of 45.22%.

Interpretation:

The study demonstrates that PAR is significantly associated with the severity of DN, suggesting its potential as a non-invasive biomarker for risk stratification in diabetic patients, which could enhance clinical decision-making.

Limitations:

• The study is limited by its retrospective design, small sample size, and potential biases inherent in such studies.
• The findings may not be generalizable to broader populations outside the study cohort.

Conclusion:

The fine-tuning-free fusion LLM framework shows promise for scalable and efficient risk stratification in diabetic nephropathy using PAR as a biomarker.