Access-related Infection and Pre-infection Albumin in Hemodialysis
Bibliographic record
Abstract
Infection of hemodialysis (HD) access is a major cause of morbidity and mortality. Certain conditions predisposing to infection (malnutrition, chronic inflammation) are associated with hypoalbuminemia. To test whether pre-existing hypoalbuminemia has any relationship with HD access infections, we analyzed the records of 87 patients on chronic HD who had access-related infection as the reason for hospital admission between July 1999 and June 2001. We obtained data on age, gender, pre-infection albumin levels, co-morbidities, complications, type of infection, infecting organism, mode of management and mortality. We compared average pre-infection albumin levels of 79 patients with access infection with those of 198 control patients on chronic HD during the study period without documented access infection. We also compared mortalities between patients with HD tunneled catheter infection treated with antibiotics alone and those treated with antibiotics plus access removal. The mean pre-infection serum albumin was lower in subjects with access infection than those without access infection (2.4 ± 0.6 vs. 3.2 ± 0.6 g/dL, P < 0.0001). Logistic regression including several clinical confounders among its candidate variables identified hypoalbuminemia as a strong predictor (P < 0.0001) of access infection. The odds ratio (OR) of access infection increased progressively with decreasing pre-infection serum albumin: OR of access infection was 8.0 (95% confidence interval {CI} 4.5–16.6) for albumin ≤ 3.0 g/L, 11.0 (95% CI 4.4–22.3) for albumin ≤ 2.5 g/dL, and 27.9 (95% CI 6.1–128.1) for albumin ≤ 2.0 g/dL. Women had a marginally higher chance of tunneled HD catheter infection than men (P = 0.08). Case mortality was 25%(4 in 16) in patients with tunneled HD catheter infection treated with antibiotics alone and 2.8%(2 in 71) in those treated with antibiotics plus access removal or change over a guide wire (P = 0.0096). Hypoalbuminemia, a predictor of adverse outcomes in HD, is associated with an increased risk of HD access infection. Women with tunneled HD catheter have a tendency to higher rates of access infection than men. Treatment of tunneled HD catheter infection with antibiotics alone is associated with increased risk of death.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".