Seeing Eye to Eye: The Key to Reducing Catheter Use
Bibliographic record
Abstract
PURPOSE: Hemodialysis central venous catheters (CVCs) are increasingly used, despite a prevalence target of <10%. The primary aim of our study was to understand why patients persistently use their CVCs. METHODS: A multicenter prospective observational study surveyed 322 patients and their vascular access coordinators (VACs) to determine the reasons patients use CVCs. Their responses were compared using multirater kappa statistics. An 18-month follow-up survey was applied to a subgroup of patients consistently using their CVCs, and correlated with the VACs' and patients' previous responses. Predictive associations for specific reasons for CVC use were explored. RESULTS: Patients indicated "non-medical" reasons (34.8%), having previously failed fistulas/grafts (25.8%), and fear of disfiguration (11.5%) as the main reasons for CVC use. The VAC was in agreement with the patient 16.5% of the time, in partial agreement 37.0%, and in disagreement 46.5%. Twelve percent of patients indicated a desire to change their CVC, yet the VAC was unaware of this 78% of the time. CONCLUSIONS: The primary reasons patients use CVCs are "non-medical" followed by concerns with the complications and esthetic appearance associated with fistulas/grafts. The significant discordance between the reasons the patients give and the VAC's view of patient reasons for CVC use suggests a gap in knowledge, understanding, or communication between patients and their VACs. Timely predialysis education to address this gap and realistic targets are necessary to reduce CVC prevalence.
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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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".