Late acceleration of glomerular filtration rate decline is a risk for hemodialysis catheter use in patients with established nephrology chronic kidney disease care
Bibliographic record
Abstract
Chronic kidney disease (CKD) patients with established nephrology care have a high rate of tunneled dialysis catheters (TDC) as first vascular access when transitioning to hemodialysis (HD). We sought to identify factors associated with this problem. Patients who started HD and had prior CKD care within our renal clinic were categorized according to access type at incident HD. Clinical factors, all estimated glomerular filtration rates (eGFR), renal clinic attendance records, hospital admissions in the 6 months preceding HD start, and patient participation in predialysis education course were analyzed. Three hundred thirty-eight patients initiated HD, 107 received pre-HD CKD care within our clinics. Seventy patients started with a TDC. All groups started HD at similar eGFR values. The trajectory of eGFR decline in the 6 months prior to HD start was significantly more rapid in the TDC group. Patients in the TDC group had more acute health events in the prior 6 months. Multivariate modeling showed that failure to attend a predialysis education course and having a more rapid rate of eGFR decline in the 6 months prior to dialysis initiation were both associated with TDC use. Patients with CKD nephrology care who initiated HD with a TDC as first vascular access had a more rapid rate of decline in eGFR in the months preceding dialysis start and were less likely to have attended our predialysis education course. This appears to correspond with the observed increased number of emergency and hospital visits in the 6 months prior to end-stage renal disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".