Classification of longitudinal estimated glomerular filtration rate trajectories in Canadian adults with type 1 diabetes
Bibliographic record
Abstract
Type 1 diabetes (T1D) increases the risk of chronic kidney disease (CKD) development. The aims of this study were to classify trajectories of estimated glomerular filtration rate (eGFR) in a cohort of Canadian adults with T1D, and to describe the risk factors associated with declining eGFR trajectories. In this retrospective cohort of adults with T1D, data was collected between 1996 and 2020. CKD was defined as eGFR <60 mL/min/1.73 m 2 . Latent class mixed models were used to categorize eGFR trajectories. Multinomial logistic regression was used to identify factors associated with declining eGFR trajectories. In this study, 304 adults were analyzed, with baseline measurements at a median duration of T1D of 15.3 (5.4–24.2) years. Eight percent of the cohort developed CKD over a median duration of 24.3 (13.7–34.8) years. Four classes of longitudinal eGFR trajectories were identified, broadly categorized as steeply declining (SD1, SD2) and gradual declining (GD1, GD2). Female sex, poor glycemic control, elevated body mass index, and albuminuria were associated with a steeply declining trajectory. In this cohort, four distinctive eGFR trajectories were identified, including a subtype with steeply declining eGFR. Given the complex nature of CKD progression, further prospective study of this model for identification of individuals at risk for CKD based on their trajectory of kidney function may support clinicians in their decision-making. • The effects of high blood sugar on the kidneys are complex. • People with type 1 diabetes are at risk for developing chronic kidney disease. • Early identification of those at risk for chronic kidney disease is advantageous. • Tracking change in kidney function may be a helpful tool for diabetes providers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".