IMPACT CKD: Holistic Disease Model Projecting 10-Year Population Burdens
Bibliographic record
Abstract
Introduction The significant burden of chronic kidney disease (CKD) is not recognized as a global public health priority, although policies aimed at delaying progression to later stages are required. Therefore, there is need for a holistic disease model to inform decision making those accounts for the multidimensional impact of CKD, and the interrelated factors that modulate progression. Methods IMPACT CKD is a microsimulation model that simulates CKD progression and incorporates the effect of clinical events and comorbidities. CKD status is assigned using estimated glomerular filtration rate (eGFR) and albuminuria levels, and CKD progression is predicted by an annual eGFR decline rate. The model projects clinical, health care resource use, economic, patient, societal, and environmental burdens from 2022 to 2032. During development, face, technical, and external validity were evaluated, with calibration conducted to population data. Further, cross-validation was conducted against 2 published models. The United Kingdom (UK) was selected as the case study for validation. Results A 7.7% increase in the CKD population by 2032 was predicted, with increasing numbers of patients with CKD stage 3 to 5 (21.7%), dialysis (75.3%), and transplantation (58.7%). The increase of patients on renal replacement therapy (RRT) results in an increase of 75% across freshwater use, fossil fuel depletion, and CO 2 emissions over the next decade, and an estimated cost of £1.95 billion in 2032. Projections reflect validated findings from other models. Conclusion The IMPACT CKD model is a robust simulation that delivers validated forecasts of the holistic CKD burden, which can support evaluation of diverse health policies and treatment strategies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".