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South Asian and Muslim Canadian Patients are less Likely to Receive Living Donor Kidney Transplant offers Compared to Caucasian, Non-Muslim Patients

2018· article· en· W2884029079 on OpenAlexaffabout
Abeera Ali, Ayub Ali, Candice Richardson, Nathaniel Edwards, Tibyan Ahmed, Márta Novák, István Mucsi

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of TorontoUniversity Health NetworkToronto General Hospital
Fundersnot available
KeywordsMedicineEthnic groupKidney donationDemographyLogistic regressionKidney transplantationDonationKidney diseaseInternal medicineTransplantation

Abstract

fetched live from OpenAlex

Background Patients with End-Stage Kidney Disease (ESKD) who belong to ethnocultural minorities are less likely to receive Living Donor Kidney Transplant (LDKT) compared to Caucasian patients. However, no studies have assessed whether Muslim Canadians face similar barriers to accessing LDKT. We explored how frequently Muslim or South Asian patients with ESKD receive LDKT offers and if they had a potential living donor (LD) identified and how this compares to Caucasian patients. Methods We used a cross-sectional, convenience sample of ESKD patients over 18 years of age from several hospitals in the Greater Toronto Area. Non-English speaking patients and patients unwilling to consent were excluded. Based on self-identified religious affiliation and ethnicity patients were grouped as: 1) Muslims 2) Caucasian, Non-Muslims 3) South Asian, Non-Muslims 4) Non-Caucasian, Non-Muslims. Patients were asked whether anyone had offered to be a living donor for them and if they had a LD identified (outcome variables). Univariable and multivariable logistic regression was used to analyze the association between religion/ethnicity and outcome variables in STATA14. Results Out of 367 participants 5% (18) were Muslim, 37% (134) Caucasian, non-Muslim and 12% (44) South Asian, non-Muslim. The mean (±SD) age was 58(±13) years, 60%(221) were male. Muslim patients tended to be younger in comparison to Caucasian, non-Muslims (53[±14] versus 58[±14] years, p=0.074). Compared to Caucasian, Non-Muslims, Muslim patients tended to be less likely to report receiving an offer for living donation (OR=0.47, CI: 0.17-1.30, P=0.147), although the association was significant only after adjusting for age, gender, and education (OR=0.32, CI: 0.11-0.96, P=0.043). South Asian, Non-Muslims, in comparison to Caucasian, Non-Muslims, also seemed to be less likely to report receiving an offer for living donation (OR=0.64, CI: 0.32-1.25, P=0.190). The association tended to be significant after adjusting for age, gender, and education (OR=0.48, CI: 0.23-1.01, P=0.052). Qualitatively similar results were seen for “having a potential living donor identified” in both groups. Discussion Muslim patients with ESKD are less likely to receive a living kidney donation offer than Caucasian, Non-Muslims. A similar trend was seen for South Asian non-Muslims. An unclear understanding of the Islamic perspective on organ donation and additional cultural or religious factors may contribute to the observed inequity. Limitations in this study are the small number of Muslim and South Asian patients, and cross-sectional design. Conclusion Muslim and South Asian patients with ESKD are less likely to receive a living kidney donation offer compared to Caucasian, Non-Muslims. These patients are, therefore, less likely to receive a LDKT. Culturally competent education may help to reduce these inequities.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.617
Threshold uncertainty score0.762

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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".

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Citations6
Published2018
Admission routes2
Has abstractyes

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