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Record W2739262366 · doi:10.1097/tp.0000000000001769

KDIGO Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors

2017· article· en· W2739262366 on OpenAlexaff
Krista L. Lentine, Bertram L. Kasiske, Andrew S. Levey, Patricia L. Adams, Josefina Alberú, Mohamed A. Bakr, Lorenzo Gallon, Catherine Garvey, Sandeep Guleria, Philip Kam‐Tao Li, Dorry L. Segev, Sandra J. Taler, Kazunari Tanabe, Linda Wright, Martin Zeier, Michael Cheung, Amit X. Garg

Bibliographic record

VenueTransplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsGuidelineMedicineRisk assessmentSystematic reviewCritical appraisalIntensive care medicineEvidence-based medicineMEDLINEHealth careFamily medicineAlternative medicinePathologyComputer science

Abstract

fetched live from OpenAlex

The 2017 Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors is intended to assist medical professionals who evaluate living kidney donor candidates and provide care before, during and after donation. The guideline development process followed the Grades of Recommendation Assessment, Development, and Evaluation (GRADE) approach and guideline recommendations are based on systematic reviews of relevant studies that included critical appraisal of the quality of the evidence and the strength of recommendations. However, many recommendations, for which there was no evidence or no systematic search for evidence was undertaken by the Evidence Review Team, were issued as ungraded expert opinion recommendations. The guideline work group concluded that a comprehensive approach to risk assessment should replace decisions based on assessments of single risk factors in isolation. Original data analyses were undertaken to produce a "proof-in-concept" risk-prediction model for kidney failure to support a framework for quantitative risk assessment in the donor candidate evaluation and defensible shared decision making. This framework is grounded in the simultaneous consideration of each candidate's profile of demographic and health characteristics. The processes and framework for the donor candidate evaluation are presented, along with recommendations for optimal care before, during, and after donation. Limitations of the evidence are discussed, especially regarding the lack of definitive prospective studies and clinical outcome trials. Suggestions for future research, including the need for continued refinement of long-term risk prediction and novel approaches to estimating donation-attributable risks, are also provided.In citing this document, the following format should be used: Kidney Disease: Improving Global Outcomes (KDIGO) Living Kidney Donor Work Group. KDIGO Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors. Transplantation. 2017;101(Suppl 8S):S1-S109.

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)0.014
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.056
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0060.006
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0050.003
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0100.011

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 head0.073
GPT teacher head0.429
Teacher spread0.356 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations570
Published2017
Admission routes1
Has abstractyes

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