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Record W2323467473 · doi:10.1097/tp.0000000000000843

A Survey of Increased Infectious Risk Donor Utilization in Canadian Transplant Programs

2015· article· en· W2323467473 on OpenAlexaffabout
Deepali Kumar, Atul Humar, S. Joseph Kim, Bryce Kiberd

Bibliographic record

VenueTransplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsQueen Elizabeth II Health Sciences CentreCNIB FoundationCapital District Health Authority
Fundersnot available
KeywordsNatListing (finance)MedicineSerologyOrgan transplantationTransplantationEnvironmental healthFamily medicineInternal medicineImmunologyBusinessComputer scienceAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: Donors at increased risk of transmitting viral infections are a potential source of transplantable organs. Studies demonstrate that organs from increased risk donors (IRDs) are associated with excellent outcomes. However, considerable variation in practice likely exists. METHODS: We performed a cross-country survey of Canadian Organ Transplant centers to determine organ utilization practices from IRDs. RESULTS: Of 40 surveys sent to transplant programs across Canada, 24 (60%) were returned. Of those, 60.9% (15/24) had a formal policy for their use, and 21.7% (5/24) had never accepted an IRD. Only 41.7% (10/24) had access to timely nucleic acid testing (NAT), and respondents were more likely to accept IRD if NAT was available. For example the likelihood of using organs from an intravenous drug user increased from 12.5% (4/24) with serology negative donors to 70.8% (17/24) if NAT was available and the donor had no increased activity within the window period (P < 0.001). Only 37.5% (9/24) discussed the use of IRDs with candidates at listing, with 54.2% (13/24) stating that having a standardized consent would increase utilization of IRDs. CONCLUSIONS: The results suggest that availability of NAT would increase IRD utilization. In addition written policies and procedures on IRD use and the consent process would be recommended in many Canadian centers.

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 score0.256
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.051
GPT teacher head0.289
Teacher spread0.238 · 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

Citations13
Published2015
Admission routes2
Has abstractyes

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