International Network of Chronic Kidney Disease cohort studies (iNET-CKD): a global network of chronic kidney disease cohorts
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a global health burden, yet it is still underrepresented within public health agendas in many countries. Studies focusing on the natural history of CKD are challenging to design and conduct, because of the long time-course of disease progression, a wide variation in etiologies, and a large amount of clinical variability among individuals with CKD. With the difference in health-related behaviors, healthcare delivery, genetics, and environmental exposures, this variability is greater across countries than within one locale and may not be captured effectively in a single study. METHODS: Studies were invited to join the network. Prerequisites for membership included: 1) observational designs with a priori hypotheses and defined study objectives, patient-level information, prospective data acquisition and collection of bio-samples, all focused on predialysis CKD patients; 2) target sample sizes of 1,000 patients for adult cohorts and 300 for pediatric cohorts; and 3) minimum follow-up of three years. Participating studies were surveyed regarding design, data, and biosample resources. RESULTS: Twelve prospective cohort studies and two registries covering 21 countries were included. Participants age ranges from >2 to >70 years at inclusion, CKD severity ranges from stage 2 to stage 5. Patient data and biosamples (not available in the registry studies) are measured yearly or biennially. Many studies included multiple ethnicities; cohort size ranges from 400 to more than 13,000 participants. Studies' areas of emphasis all include but are not limited to renal outcomes, such as progression to ESRD and death. CONCLUSIONS: iNET-CKD (International Network of CKD cohort studies) was established, to promote collaborative research, foster exchange of expertise, and create opportunities for research training. Participating studies have many commonalities that will facilitate comparative research; however, we also observed substantial differences. The diversity we observed across studies within this network will be able to be leveraged to identify genetic, behavioral, and health services factors associated with the course of CKD. With an emerging infrastructure to facilitate interactions among the investigators of iNET-CKD and a broadly defined research agenda, we are confident that there will be great opportunity for productive collaborative investigations involving cohorts of individuals with CKD.
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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 | 0.035 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".