Determining Factors That Predict Technique Survival on Peritoneal Dialysis: Application of Regression and Artificial Neural Network Methods
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND/AIMS: Peritoneal dialysis (PD) rates continue to decline worldwide in spite of the increasing number of patients with end-stage renal disease. PD technique failure has been cited as one of the reasons for this decline. The purpose of this study was to compare the factors that predict technique survival using artificial neural network (ANN) and logistic and Cox regression methods. METHODS: We used high-quality, prospectively collected data from the United Kingdom Renal Registry and created both ANN and regression models to predict technique survival. Incident PD patients in the UK from 1999 to 2004 were included in the analysis. Technique failure was defined as a change in modality to hemodialysis for a period >30 days. RESULTS: Removal of dialysis center code had a significant effect on the fit and/or predictive performance of all three types of models. In contrast, the effect of demographic data, comorbidity, physical examination and laboratory data varied according to the type of model. CONCLUSIONS: PD center significantly impacts PD technique survival. Other putative predictive factors had marginal and/or variable effects. The presence of comorbid conditions and a high body mass index is not consistently associated with increased PD technique failure.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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