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Record W2504373910 · doi:10.1111/ajt.13997

Towards Improving the Transfer of Care of Kidney Transplant Recipients

2016· article· en· W2504373910 on OpenAlexaff
John S. Gill, Alissa Wright, Francis L. Delmonico, Kenneth A. Newell

Bibliographic record

VenueAmerican Journal of Transplantation · 2016
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British Columbia
Fundersnot available
KeywordsMedicineKidney transplantIntensive care medicineHealth careKidney transplantationPoint of careTransmission (telecommunications)Family medicineMedical emergencyTransplantationNursingInternal medicine

Abstract

fetched live from OpenAlex

Kidney transplant recipients require specialized medical care and may be at risk for adverse health outcomes when their care is transferred. This document provides opinion-based recommendations to facilitate safe and efficient transfers of care for kidney transplant recipients including minimizing the risk of rejection, avoidance of medication errors, ensuring patient access to immunosuppressant medications, avoidance of lapses in health insurance coverage, and communication of risks of donor disease transmission. The document summarizes information to be included in a medical transfer document and includes suggestions to help the patient establish an optimal therapeutic relationship with their new transplant care team. The document is intended as a starting point towards standardization of transfers of care involving kidney transplant recipients.

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)0.038
metaresearch head score (Gemma)0.119
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.119
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0060.002
Scholarly communication0.0100.009
Open science0.0040.013
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0230.014

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 head0.011
GPT teacher head0.269
Teacher spread0.258 · 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".

Quick stats

Citations22
Published2016
Admission routes1
Has abstractyes

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