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Record W2554501803 · doi:10.1159/000450979

Disparities in Kidney Transplantation Access among Korean Patients Initiating Dialysis: A Population-Based Cohort Study Using National Health Insurance Data (2003-2013)

2016· article· en· W2554501803 on OpenAlexaff
Young Choi, Jaeyong Shin, Jung Tak Park, Kyoung Hee Cho, Eun‐Cheol Park, Tae Hyun Kim

Bibliographic record

VenueAmerican Journal of Nephrology · 2016
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsInstitute of Health Services and Policy Research
Fundersnot available
KeywordsMedicineDialysisHazard ratioKidney transplantationTransplantationKidney diseaseSocioeconomic statusInternal medicineNephrologyCohort studyCohortPopulationIntensive care medicineEnvironmental healthConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: The socioeconomic status of a person has an impact on his or her access to kidney transplantation as has been reported in western countries. This study examined the association between income level and kidney transplantation among chronic kidney disease patients undergoing dialysis in South Korea. METHODS: We analyzed data from 1,792 chronic kidney disease patients undergoing dialysis and listed in the Korean National Health Insurance Claim Database (2003-2013). The likelihood of receiving the first kidney transplant over time was analyzed using competing risk proportional hazard models on time from initiating dialysis to receiving a transplant. RESULTS: Of 1,792 patients on dialysis, only 184 patients (10.3%) received kidney transplants. Patients with medical aid had the lowest kidney transplantation rate (hazard ratio 0.29, 95% CI 0.16-0.51). A lower income level was significantly associated with a low kidney transplantation rate, after adjusting for covariates, compared to patients in the high-income level group. CONCLUSIONS: Our findings indicate that in South Korea, the total number of kidney transplants is remarkably low and there exists income disparity with regard to access to kidney transplantation. Thus, we suggest that plans be implemented to encourage organ donation and increase organ transplant accessibility for all patients irrespective of their socioeconomic status.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.352
Teacher spread0.312 · 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 teacher head, 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

Citations6
Published2016
Admission routes1
Has abstractyes

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