Impact of Change in Allocation Score Methodology on Post Kidney Transplant Average Length of Stay
Bibliographic record
Abstract
Background: In December 2014, a new Kidney Allocation System (KAS) was implemented nationwide to improve access and quality of care to historically disadvantaged patients. However, no study to date has examined the relationship between the KAS and potential changes in hospital length of stay (LOS). This study aimed to examine the relationship between the KAS implemented in December 2014 and potential changes in hospital LOS. Methods: We used data from the Florida Agency for Health Care Administration on kidney transplant surgeries completed between 2011 and 2018. A cross-sectional cohort study design included seven hospitals that performed kidney transplants for the duration of the study. A propensity score matching approach was used to examine the relationship between KAS and LOS. All acute general medical and surgical hospitals in Florida that performed kidney transplant surgery were included in the analysis. Results: We included 7,795 patients, 6,119 discharged to home, and 1,676 discharged to home with home health services after transplant. The average LOS prior to KAS was 6.52 days and 6.08 days post KAS. Propensity matched results show that patients transferred to home experienced a decrease in the LOS (coefficient (β) = -0.68; 95% confidence interval (CI): -0.95, -0.42) after the new allocation score was implemented. Similarly, patients transferred to home with home health experienced a decrease in the LOS (β = -1.90; 95% CI: -2.69, -1.11) after the new allocation was implemented. Conclusion: In conclusion, results indicate that KAS implementation did not add a burden on the health system by increasing LOS when considering patients with similar characteristics before and after KAS implementation. KAS is an important policy change that appears to not negatively affect the LOS when sicker patients could receive a kidney transplant. Our findings improve our understanding of the KAS policy and its influence on the health system.
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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 | 0.046 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".