Bibliographic record
Abstract
Twenty years ago, doctors were taught thatpatients with Type 2 diabetes would devel-op large vessel disease, but they would not develop small vessel disease or those types of complications seen commonly in Type 1 dia-betic patients, such as nephropathy and retinopathy. However, diabetes is now the lead-ing cause of kidney disease and of end-stage renal disease (ESRD) requiring renal replace-ment treatments, such as dialysis. In Canada, 30 % of new dialysis patients have diabetes; in the U.S., 40 % of new renal patients have dia-betes. The vast majority of these diabetic patients have Type 2 diabetes.1 Factors Increasing the Risk of ESRD It is currently unclear whether the process of kidney damage is identical for patients with Type 1 and Type 2 diabetes. Most of the clinical research in the past was done with patients who have Type 1 diabetes, although most of the cur-rent research looks into Type 2 diabetes. It is believed that genetics is an important factor in predicting which patients will develop diabetic renal disease.2 However, the control of blood sugar levels has been considered the most important modifiable risk factor for the preven-tion of initial renal damage and an important modifiable risk factor in the progression of dia-betic renal disease to ESRD (Table 1). Control All patients with diabetic nephropathy and one-third of patients with Type 2 diabetes will have hypertension. This article will discuss the factors leading to this condition and multi-factoral treatment considerations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".