Trends in Early Hospital Readmission After Kidney Transplantation, 2002 to 2014
Bibliographic record
Abstract
BACKGROUND: Early hospital readmission (EHR) is associated with morbidity, mortality, and significant healthcare costs. However, trends over time in EHR events in kidney transplant recipients have not been examined. We conducted a population-based cohort study using linked healthcare databases from Ontario, Canada, to determine whether the EHR incidence has changed from 2002 to 2014 in kidney transplant recipients. METHODS: We defined EHR as an unplanned admission for any reason to an acute care hospital within 30 days of being discharged from the hospital for transplantation; admissions for elective procedures were excluded. RESULTS: We included 5437 kidney transplant recipients. More recently transplanted recipients (2011 to 2014 vs 2002 to 2004) were older and more likely to have coronary artery disease. A total of 1128 (20.7%) kidney transplant recipients experienced an EHR. There was no trend in EHR across eras with a 30-day cumulative incidence of 23.0%, 21.4%, 18.4%, and 21.0% (P for trend =0.197) for the years 2002 to 2004, 2005 to 2007, 2008 to 2010, and 2011 to 2014, respectively. In the multivariable Cox proportional hazards model, we found no association between era of transplant and EHR. When examining variation in EHR across the 6 adult transplant centers, we found the 30-day cumulative incidence varied significantly from 15.5% to 27.1% (P < 0.001). CONCLUSIONS: One in 5 kidney transplant recipients will experience an EHR; however, an increase in EHR over time has not been observed despite increasing recipient age and comorbidities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".