Incidence and Risk Factors Involved in the Development of Nephropathy in Patients with Type 1 Diabetes Mellitus: Follow Up Since Onset
Bibliographic record
Abstract
OBJECTIVES: Estimation of the incidence of nephropathy as well as potential risk factors involved in its onset in a cohort of patients with type 1 diabetes who were followed from diagnosis. METHODS: We studied 716 patients, who were followed for a mean (standard deviation [SD]) of 10.1 (SD: 5.3) years. We analyzed the influence of demographic characteristics and levels of glycated hemoglobin (A1C), lipids and blood pressure during the course of the disease by univariate and multivariate survival methods. RESULTS: The cumulative incidence of nephropathy was 2.6%, 6.3% and 11.9% at 5, 10 and 15 years of evolution, respectively. The factors associated with increased risk for nephropathy were systolic blood pressure and A1C levels. An increment of 10 mm Hg in systolic blood pressure increases the risk by 36%, and an increment of 1% in A1C levels raises the risk by 13% at 5 years since onset and 68% at 10 years, and it doubles the risk at 15 years. Women have higher risk than men (hazard ratio 1.79; p=0.024). CONCLUSIONS: Our study suggests that female gender and high levels of A1C and systolic blood pressure throughout the course of the disease are the main factors associated with an increased risk for development of nephropathy in patients with type 1 diabetes mellitus.
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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 | 0.001 |
| 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".