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Access to living donor transplantation for Aboriginal recipients: a study of living donor presentation and exclusion

2011· article· en· W1558660920 on OpenAlexaff
Sara Dunsmore, Martin Karpinski, Ann Young, Leroy Storsley

Bibliographic record

VenueClinical Transplantation · 2011
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsWestern UniversityUniversity of Manitoba
Fundersnot available
KeywordsMedicineTransplantationPresentation (obstetrics)Living donor liver transplantationGerontologySurgeryLiver transplantation

Abstract

fetched live from OpenAlex

Aboriginals experience high rates of end-stage renal disease (ESRD) and are less likely to receive a kidney transplant from a living donor. We hypothesized that higher rates of hypertension and diabetes in Aboriginal communities would result in fewer potential living donors coming forward and more exclusions for medical reasons. We performed a retrospective study to examine the frequency of potential donor presentation and the reasons for donor exclusion among Aboriginal and Caucasian wait-listed ESRD patients at our center. Three hundred and eighty-five wait-listed patients were studied, including 174 Aboriginals and 211 Caucasians. Time on the waiting list was similar between groups. A similar proportion of Aboriginals and Caucasians had at least one potential donor (40% vs. 46%), and the rate of donor exclusion for medical reasons was also similar (23% vs. 21%). Potential donors to Aboriginals were more likely to be excluded for non-medical reasons (50% vs. 30%; p < 0.0001), of which 96% were because of loss of contact. Waitlisted Aboriginal ESRD patients appear just as likely as Caucasians to have potential living donors initiate evaluation and have a similar rate of donor exclusion because of medical reasons. Further work is required to identify why donors to Aboriginals are more likely to withdraw from the evaluation process.

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)0.001
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 score0.057
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.111
GPT teacher head0.427
Teacher spread0.316 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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