Predictive models on patients’ eligibility for peritoneal dialysis
Bibliographic record
Abstract
BackgroundPeritoneal dialysis (PD) is being promoted because it is cost-effective and has equivalent outcomes to facility-based hemodialysis (HD). Determining PD eligibility is critical but subjective, with high variability among renal programs. This study aimed to establish a predictive model for PD eligibility among individuals who started treatment with HD. A secondary objective was to identify predictors of PD eligibility and determine if eligible patients went on to receive PD.MethodsThis retrospective cohort study included individuals starting HD at multiple hospitals in Alberta, Canada, as part of the START program between 1 October 2016 and 31 March 2018. Twenty-seven predictors, including patient characteristics, laboratory values, and comorbidities, were considered in logistic regression modeling. The outcome variable was PD eligibility, as determined by a standardized interdisciplinary assessment. The model selection was based on the Akaike information criterion. The confusion matrix was used for each model to compare the predicted versus observed eligibility. The final model was calibrated and presented.ResultsAmong the 598 participants, 391 (65.4%) were considered eligible for PD. The logistic regression model achieved a modest performance in discriminating patients who were eligible for PD, with a high sensitivity of 91.3%, an accuracy of 0.68 (95% CI, 0.65-0.72), and an area under the receiver operating characteristic curve ranging from 0.69 to 0.71. Age (OR = 0.98; 95% CI, 0.97-0.99), body mass index (OR = 0.95; 95% CI, 0.93-0.97), starting dialysis in intensive care unit (OR = 0.53; 95% CI, 0.31-0.92), and polycystic kidney disease (OR = 0.37; 95% CI, 0.13-0.99) were statistically significant factors associated with a lower likelihood of being considered eligible for PD. Out of the 391 eligible PD patients, 87 (22.3%) received PD treatment within 6 months of starting HD.ConclusionsThe majority of patients starting HD were considered eligible for PD. Our model exhibits a high level of sensitivity and could serve as a valuable tool for screening potential candidates following the commencement of HD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".