WCN25-1654 SOCIOECONOMIC DISPARITIES AND SUBOPTIMAL LIPID MANAGEMENT IN PRE-DIALYSIS CHRONIC KIDNEY DISEASE PATIENTS: AN OBSERVATIONAL SINGLE CENTRE STUDY
Bibliographic record
Abstract
eligible for inclusion.Baseline demographic data including age, sex, cause of kidney disease, current treatments, other relevant medical conditions, and pathology data including estimated glomerular filtration rate (eGFR), albuminuria, cardiac biomarkers are collected from patient records by site staff at the time of recruitment after consent.Follow-up data, including eGFR, albuminuria and clinical kidney and cardiovascular outcomes are collected from medical records during routine clinical care visits.Results: The GKPTN has enrolled 4334 patients since May 2020 across 119 sites in 8 countries (United States, Australia, Argentina, China, Italy, Canada, Spain and Japan).The mean participant age (Standard Deviation) at enrolment was 64.5 (16.2) years, 2542 (58.7%) were female, 1875 (43.3%) participants had diabetic kidney disease, mean eGFR was 52.9 (29) mL/min/1.73m2,and median Urine Albumin Creatinine Ratio (interquartile range) was 89 (20, 420) mg/g.Most participants were Caucasian (N¼2267,52.3%),followed by Latino-Americans (N¼866, 20.0%), Black or African American (N¼624, 14.4%) and Asians (N¼533, 12.3%).Geographically, most patients were recruited from North America (60.5%), followed by Europe (13.2%),South America (10.9%), and Asia (10.7%).With regards to kidney protective therapies, renin angiotensin aldosterone inhibitors were used in 3145 (72.6%) participants at baseline, while sodium glucose co-transport inhibitors were used in 579 (13.3%) participants.During 0.89 (0.50, 1.44) median years of follow-up, the mean annual eGFR change was -1.7 (95%CI -2.3, -1.0) mL/min/1.73m2.The rate of eGFR decline did not vary among age or sex specific subgroups with the steepest decline in people with baseline eGFR$60 mL/min/1.73m2.Conclusions: The GKPTN has successfully recruited more than 4000 patients with a diverse patient population.The range of countriessupportsthat future findings from the GKPTN are generalizable to patients around the world.The data allow descriptions of nephrology practices around the world and highlight that use of kidney protective therapies such as renin-angiotensin aldosterone inhibitors could be optimised.I have no potential conflict of interest to disclose.I did not use generative AI and AI-assisted technologies in the writing process.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".