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Taking Specific Steps to Pursue Living Donor Kidney Transplant is Associated with Greater Odds of Receiving a Living Donor Offer

2018· article· en· W2883271523 on OpenAlexaffabout
Abeera Ali, Deanna Toews, Navneet Singh, Ayub Ali, Sarah Cao, Candice Richardson, Sumaya Dano, Punithan Thiagalingam, 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
KeywordsMedicineLogistic regressionDialysisOddsOdds ratioDonationCross-sectional studyKidney transplantationKidney diseaseDemographyTransplantationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background Living Donor Kidney Transplant (LDKT) is the optimal treatment for patients with End-Stage Kidney Disease (ESKD), however many eligible candidates are not able to receive LDKT. To our knowledge, no published studies have assessed the relationship between taking steps to pursue LDKT and the likelihood of securing an offer of living donation (OLD). We explored the relationship between patients taking specific actions to pursue LDKT and having received an OLD. Methods We used a cross-sectional, convenience sample of adult patients with ESKD from several hospitals in the Greater Toronto Area. Non-English speaking patients and patients unwilling to consent were excluded. Patients were asked whether they had taken specific steps in pursuing LDKT (exposure variables, see Table 1) and if anyone had offered to be a living donor for them (outcome variable). Univariable and multivariable logistic regression was used to analyze the association between exposure and outcome variables in STATA14. Results Data from 367 participants was analyzed. The mean (±SD) age was 58(±13) years, and 60% (221) of participants were male. Seventy one percent (260) of patients reported having greater than 12 years of education and 46% (168) were married or in a common-law relationship. The median time patients had been on dialysis was 2.1 years (IQR: 1.0-5.2 years) and 43% (156) had diabetes. Thirty eight percent (141) were White while 32% (117) were Black. Forty five percent (164) reported having received an OLD. Patients who reported having asked potential donors directly to be tested for eligibility were more likely to have received an OLD (OR=9.31, CI: 4.21-20.60, P<0.05) compared to those who had not yet asked. The association remained significant after adjusting for age, gender, education, ethnicity, marital status and transplant knowledge score (OR=6.80, CI: 2.89-16.00, P<0.05). Similarly, patients who reported having spoken to family or friends about the possibility of getting LDKT were more likely to have received an OLD (OR=6.54, CI: 3.76-11.36, P<0.05), after adjusting for age, gender, education, ethnicity, marital status and transplant knowledge score. Similar results were seen for patients who had “allowed others to tell people of their willingness to pursue LDKT”, “talked to people about their interest in LDKT” and “shared educational material about LDKT”.Discussion Taking small, specific steps towards pursuing LDKT may lead to successfully securing a living donor offer. Thus, patients who appear to be unable or unwilling to take these steps may be at a significant disadvantage. Conclusion Patients who have actively taken steps to pursue living donation are more likely to have received an OLD compared to those who have not taken these steps. Future research will be needed to identify the potentially modifiable barriers to taking these steps and to develop interventions to help patients explore the optimal treatment for ESKD.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.013
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.025
GPT teacher head0.258
Teacher spread0.233 · 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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Citations1
Published2018
Admission routes2
Has abstractyes

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