Early Hospital Readmissions After Transplantation
Bibliographic record
Abstract
BACKGROUND: Solid organ transplantation is the preferred treatment for patients with end-stage organ failure. Although much progress has been made over the past decade, some patients still require early readmission after their initial hospital discharge. Early hospital readmission is an important metric for health care quality. It is often measured in nontransplant medical and surgical conditions but has only recently been applied to organ transplantation. METHODS: We performed a structured MEDLINE search to retrieve, review, and summarize original studies on the incidence, risk factors, outcomes, and prevention of early hospital readmissions after kidney, liver, and kidney-pancreas transplantation. Early hospital readmission was defined as readmission to hospital within 30 days of discharge from the transplant hospitalization. RESULTS: The risk of early readmission varies by organ type, (highest in liver transplants and lowest in kidney transplants). Causes for early hospital readmission are most commonly due to surgical, immunologic, or infectious complications. Risk factors associated with early hospital readmission often reflect pretransplant comorbidity, and many of these factors may not be modifiable. Early hospital readmission is also associated with decreased graft and patient survival. CONCLUSIONS: Early hospital readmission after transplantation is common and associated with adverse outcomes. The potential for preventing early hospital readmissions and the impact on patient outcomes remain unclear. Current evidence suggests that some, but not all, early hospital readmissions after transplantation may be prevented.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".