Vascular Access-Related Bloodstream Infections in First Nations, Community and Teaching Canadian Dialysis Units, and Other Centre-Level Predictors
Bibliographic record
Abstract
BACKGROUND/AIMS: Vascular access-related bloodstream infection (BSI) is frequent among patients undergoing hemodialysis increasing their morbidity and mortality, but its occurrence across various dialysis centre types is not known. The aims of this study were to describe the incidence rates and assess the variability in BSI risk between dialysis centre types and other centre-level variables. METHODS: We conducted a retrospective cohort study of 621 patients initiating hemodialysis in 7 Canadian dialysis centres. Cox regression models, where access type was continuously updated, were used to identify predictors of BSI occurrence. RESULTS: During follow-up of the cohort (median age 68.1 years, 41.7% female, and 76.7% initiating with a central venous catheter, CVC), 73 patients had a BSI (rate: 0.21/1000 person-days). The BSI risk was not different in First Nation units (adjusted relative risk: 0.47, 95% confidence interval: 0.06-3.72) and teaching hospitals (1.33, 0.70-2.54) compared to community hospitals. No other centre-related variables were associated with the risk of BSI. CONCLUSION: We did not find differences in the BSI risk among dialysis unit types, or any other centre-related variables. The rates of BSI in our population were lower than those observed in other settings, but the high proportion of patients using CVCs is concerning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".