Trends in infection-related hospital admissions and impact of length of time on dialysis among patients on long-term dialysis: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: After cardiovascular disease, infection is the second leading reason for admission to hospital among patients receiving long-term dialysis. We examined whether duration of dialysis treatment influences the rate of infection-related admission to hospital. METHODS: Using provincial administrative databases for Quebec, we built a retrospective cohort of all adults receiving long-term dialysis (hemodialysis or peritoneal dialysis) between 2001 and 2007. We evaluated rates of infection-related admission to hospital according to length of time on dialysis. RESULTS: A cohort of 9822 patients (mean age 66.3 [standard deviation ± 14.7] yr; 39.7% female) were followed for a median of 2.1 (range 1.0-3.9) years. Between 2001 and 2007, infection-related hospital admissions remained stable (from 0.20 to 0.19 per person-year; p = 0.7). All-cause hospital admission rates decreased by 22.9% (from 1.53 to 1.18 per person-year; p < 0.001), and cardiovascular-related admission rates decreased by 46.7% (from 0.45 to 0.24 per person-year; p < 0.001). The rate of infection-related admission remained stable with increasing time on dialysis (p = 0.1); however, both all-cause and cardiovascular-related admission rates decreased with length of time on dialysis (p < 0.001). Standardization of hospital admission rates by age, sex or length of time on dialysis did not change trends. INTERPRETATION: We found a stable rate of infection-related hospital admission between 2001 and 2007 among patients on long-term dialysis, independent of age, sex and length of time on dialysis. A decrease in all-cause and cardiovascular-related admission rates during the same period meant that the proportion of admissions related to infection increased. Because admissions to hospital are potentially preventable, understanding the epidemiology of infection-related admissions may inform future studies on prevention of this serious outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".