Bibliographic record
Abstract
Di!erences in glycemic control and survival predict higher ESRD rates in diabetic "rst nations adults Purpose: Diabetic First Nations people (FN) have higher ESRD rates than other Canadi-ans but the reasons remain unclear. We sought to better understand this disparity by com-paring demographic, laboratory and survival features of diabetic FN and other Saskatche-wan residents (OSK) by renal function stage. Methods: Prevalent diabetes cases in 2005/06 were identi"ed in Saskatchewan’s two larg-est health regions using administrative databases, and linked with centralized laboratory tests. #ey were sub-divided into "ve stages of renal function using estimated glomerular "ltration rates (eGFR) that were determined in 992 of 2,321 FN (42.7%) and 14,054 of 21,886 OSK (64.2%). Age, sex, urine microalbumin (MA), glycosylated hemoglobin (A1C), low density lipoprotein cholesterol (LDL-C) and two year mortality risk was compared for all subjects. Results: Diabetic FN were younger (mean age 52.7 vs. 64.2, p<0.0001), more likely to be female (59.6 % vs.45.4%, p<0.001), had increased MA (56.6 % vs. 48.4%, p<0.0001) and displayed higher mean A1C levels (8.16 % vs.7.36%, p<0.0001) than OSK. Despite a larger proportion having eGFR’s>60 ml/min (87.0 % vs.77.3%, p<0.001), FN were also more likely to have ESRD (2.3 % vs.0.8%, p<0.001). Although FN with eGFR’s>30 ml/min experienced higher age/sex adjusted mortality risk than OSK, the trends for both adjusted and unadjusted mortality risks for those with advanced pre-ESRD renal failure were lower for FN than for OSK. Conclusions: Elevated rates of ESRD experienced by FN with diabetes are related to poorer glycemic control at all levels of renal function, and lower age-related mortality at advanced stages of chronic kidney disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.886 | 0.709 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".