Functional joint models for chronic kidney disease in kidney transplant recipients
Why this work is in the frame
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Bibliographic record
Abstract
This functional joint model paper is motivated by a chronic kidney disease study post kidney transplantation. The available kidney organ is a scarce resource because millions of end-stage renal patients are on the waiting list for kidney transplantation. The life of the transplanted kidney can be extended if the progression of the chronic kidney disease stage can be slowed, and so a major research question is how to extend the transplanted kidney life to maximize the usage of the scarce organ resource. The glomerular filtration rate is the best test to monitor the progression of the kidney function, and it is a continuous longitudinal outcome with repeated measures. The patient's survival status is characterized by time-to-event outcomes including kidney transplant failure, death with kidney function, and death without kidney function. Few studies have been carried out to simultaneously investigate these multiple clinical outcomes in chronic kidney disease stage patients based on a joint model. Therefore, this paper proposes a new functional joint model from this clinical chronic kidney disease study. The proposed joint models include a longitudinal sub-model with a flexible basis function for subject-level trajectories and a competing-risks sub-model for multiple time-to event outcomes. The different association structures can be accomplished through a time-dependent function of shared random effects from the longitudinal process or the whole longitudinal history in the competing-risks sub-model. The proposed joint model that utilizes basis function and competing-risks sub-model is an extension of the standard linear joint models. The application results from the proposed joint model can supply some useful clinical references for chronic kidney disease study post kidney transplantation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.365 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it