The association between urinary lgM excretion and diabetic retinopathy in diabetic patients
Bibliographic record
Abstract
BACKGROUND: Diabetic Retinopathy is one of the most common causes of blindness among adults. Microvascular complications may have common origins. The objective of the present study is to analyze the correlation between urinary IgM excretion and diabetic retinopathy based on the type of diabetes. METHODS: The present study is cross-sectional analytic and was carried out on 140 type2 diabetic patients (of which 70 patients diagnosed with retinopathy) and 76 type1 diabetic patients (of which 37 patients diagnosed with retinopathy). For every patient in each of the test groups, fasting plasma glucose, triglyceride, cholesterol, creatinin and HbA1c tests were done. The value of IgM, the albumin- to- creatinine ratio and the urine analysis test were also used to rule out the significant proteinuria of the patients. Then, IgM Index was measured using the following equation: Igm Index = Urine IgM/Urine Cr. RESULTS: The level of IgM index in the diabetic patients (type1 and type2) had no significant correlation with retinopathy. Cut point = 1.49, sensitivity = 0.703 and specificity = 0.308 in type1 diabetes were used for screen retinopathy. In type1 diabetic patients, the duration of diabetes had a significant correlation with urinary protein while in type 2 diabetic patients, the diabetes duration and HbA1c were significantly correlated with retinopathy. CONCLUSION: The results of this study demonstrate that the level of urinary IgM in diabetic patients has no difference in those who have or lack retinopathy, but the urinary IgM level of more than 1.49 mg/dl can be considered as a cut point in type1 diabetic patients to screen retinopathy.
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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".