Infectious outcomes of fibrin sheath disruption in tunneled dialysis catheters
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Fibrin sheath (FS) formation around tunneled central venous catheters (CVC) increases the risk of catheter-related bloodstream infections due to bacterial adherence to a biofilm. We sought to investigate whether FS disruption (FSD) at the time of CVC removal or exchange affects infectious outcomes in patients with CVC-related infections. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: Retrospective cohort study of 307 adult maintenance hemodialysis patients aged 18 years or older at a single center academic-based hemodialysis program (UHN, Toronto) who developed CVC-related infections requiring CVC removal or exchange between January 2000 and January 2019. Exposure was FSD at the time of CVC removal or exchange. Outcomes were infectious metastatic complications, recurrent infection with the same organism within 1 year, or death due to infection. We created a Markov Multi-State Model (MMSM) to assess patients' trajectories through time as they transitioned between states. A time-to-event analysis was performed, adjusted for clinically relevant factors. RESULTS: = 0.73). CONCLUSIONS: FSD at the time of CVC removal was not associated with increased risk of infectious complications or death due to infection. Further prospective study is needed to determine whether FSD contributes to reducing CVC infectious related complications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".