Risk of bloodstream infection in patients with renal dysfunction: a population-based cohort study
Bibliographic record
Abstract
Although patients with end-stage renal disease (ESRD) are known to be at high risk for developing bloodstream infections (BSI), the risk associated with lesser degrees of renal dysfunction is not well defined. We sought to determine the risk for acquiring and dying from community-onset BSIs among patients with renal dysfunction. A retrospective, population-based cohort study was conducted among adult residents without ESRD in the western interior of British Columbia. Estimated glomerular filtration rates (eGFR) were determined for cases and incidence rate ratios (IRR) were calculated using prevalence estimates. Overall, 1553 episodes of community-onset BSI were included of which 39%, 32%, 17%, 9%, 2% and 1% had preceding eGFRs of ≥90, 60-89, 45-59, 30-44, 15-29 and <15 ml/min/m2, respectively. As compared to those with eGFR ≥60 ml/min/m2, patients with eGFR 30-59 ml/min/m2 (IRR 4.4; 95% confidence interval (CI) 3.9-4.9) and eGFR <30 ml/min/m2 (IRR 7.0; 95% CI 5.0-9.5) were at significantly increased risk for the development of community-onset BSI. An eGFR <30 ml/min/m2 was an independent risk factor for death (odds ratio 2.3; 95% CI 1.01-5.15). Patients with renal dysfunction are at increased risk for developing and dying from community-onset BSI that is related to the degree of dysfunction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".