Catheter Event Rates in Medical Compared to Surgical Peritoneal Dialysis Catheter Insertion
Bibliographic record
Abstract
Introduction: How patient, center, and insertion technique factors interact needs to be understood when designing peritoneal dialysis (PD) catheter insertion pathways. Methods: We undertook a prospective cohort study in 44 UK centers enrolling participants planned for first catheter insertion. Sequences of regressions were used to describe the associations linking patient and dialysis unit-level characteristics with catheter insertion technique and their impact on the occurrence of catheter-related events in the first year (catheter-related infection, hospitalization, and removal). Factors associated with catheter events were incorporated into a multistate model comparing the rates of catheter events between medical and surgical insertion alongside treatment modality transitions and mortality. Results: Of 784 first catheter insertions, 466 (59%) had a catheter event in the first year and 61.2% of transitions onto hemodialysis (HD) were immediately preceded by a catheter event. Catheter malfunction was less but infection was more common with surgical compared with medical insertions. Participants at centers with fewer late presenters and more new dialysis patients starting PD, had a lower probability of a catheter event. Adjusting for these factors, the hazard ratio for a catheter event following insertion (medical vs. surgical) was 0.70 (95% confidence interval [CI] 0.43 to 1.13), and once established on PD 0.77 (0.62 to 0.96). Conclusion: Offering both medical and surgical techniques is associated with lower catheter event rates and keeps people on PD for longer.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".