Association of hospital transfusion use and infection‐related rehospitalizations among patients receiving hemodialysis: A retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Red blood cell transfusions have been associated with infection risk. We investigated whether hospital transfusions are associated with infections in maintenance hemodialysis patients requiring transfusions for chronic anemia. METHODS: In this retrospective cohort study, hemodialysis patients who experienced an incident hospitalization during 2012-2013 were identified from the Medicare end-stage renal disease database. Hospital transfusions were first categorized into one of five groups based on adjusted likelihood of administering red blood cell transfusions during inpatient hospital stays that occurred over the previous year (2011) among the general Medicare cohort. Next, in a patient-level analysis, patients were categorized according to transfusion use at the incident hospitalization hospital. Outcomes were infection-related rehospitalization and a composite of infection-related hospitalization and all-cause mortality during the 60 days following hospital discharge. We estimated adjusted rate ratios for the association between hospital transfusion use and risk of rehospitalization or the composite endpoint using Poisson regression models. FINDINGS: The study included 1578 hospitals and 61,455 hemodialysis patients. Patient characteristics were balanced across hospital transfusion use groups. The overall transfusion rate was 16.0%. The overall 30-day infection-related hospitalization rate (95% confidence interval) per 100 patient-months was 8.8 (8.6-9.1); rates did not differ by transfusion use group. Rate ratios for infection-related rehospitalization were 1.00 (0.91-1.10) over 30 days and 0.98 (0.91-1.05) over 60 days comparing the lowest and highest transfusion use groups. DISCUSSION: We found no differences in risk of infection-related rehospitalization for patients receiving maintenance hemodialysis across the varying blood transfusion rates of US hospitals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".