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Record W4221028649 · doi:10.14740/jocmr4673

Impact of Change in Allocation Score Methodology on Post Kidney Transplant Average Length of Stay

2022· article· en· W4221028649 on OpenAlexvenueno aff
Hanadi Hamadi, Hani M. Wadei, Jing Xu, Dayana Martinez, Aaron Spaulding, Shehzad K. Niazi, Tambi Jarmi

Bibliographic record

VenueJournal of Clinical Medicine Research · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsnot available
FundersUniversity of North FloridaFlorida Agency for Health Care Administration
KeywordsMedicinePropensity score matchingConfidence intervalKidney transplantHealth careCohortEmergency medicineInternal medicineKidney transplantationKidney

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.046
metaresearch head score (Gemma)0.123
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.123
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.635
GPT teacher head0.622
Teacher spread0.013 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

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