MétaCan
Menu
Back to cohort
Record W2897355601 · doi:10.1177/2054358118801589

Updated Canadian Expert Consensus on Assessing Risk of Disease Progression and Pharmacological Management of Autosomal Dominant Polycystic Kidney Disease

2018· article· en· W2897355601 on OpenAlexaffabout
Steven Soroka, Ahsan Alam, Micheli Bevilacqua, Louis Girard, Paul Komenda, Rolf Loertscher, Philip A. McFarlane, Sanjaya Pandeya, Paul Tam, Daniel G. Bichet

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsUniversité de MontréalHalTechUniversity of TorontoLakeshore General HospitalSt. Michael's HospitalThe Scarborough HospitalSeven Oaks General HospitalUniversity of ManitobaFoothills Medical CentreHôpital du Sacré-Cœur de MontréalMcGill UniversityUniversity of CalgaryUniversity of British ColumbiaRoyal Victoria HospitalDalhousie University
FundersOtsuka Pharmaceutical
KeywordsTolvaptanAutosomal dominant polycystic kidney diseaseMedicineIntensive care medicinePolycystic kidney diseaseGenetic testingDiseasePKD1Kidney diseaseInternal medicineBioinformatics

Abstract

fetched live from OpenAlex

Purpose: The purpose of this article is to update the previously published consensus recommendations from March 2017 discussing the optimal management of adult patients with autosomal dominant polycystic kidney disease (ADPKD). This document focuses on recent developments in genetic testing, renal imaging, assessment of risk regarding disease progression, and pharmacological treatment options for ADPKD. Sources of information: Published literature was searched in PubMed, the Cochrane Library, and Google Scholar to identify the latest evidence related to the treatment and management of ADPKD. Methods: All pertinent articles were reviewed by the authors to determine if a new recommendation was required, or if the previous recommendation needed updating. The consensus recommendations were developed by the authors based on discussion and review of the evidence. Key findings: The genetics of ADPKD are becoming more complex with the identification of new and rarer genetic variants such as GANAB. Magnetic resonance imaging (MRI) and computed tomography (CT) continue to be the main imaging modalities used to evaluate ADPKD. Total kidney volume (TKV) continues to be the most validated and most used measure to assess disease progression. Since the publication of the previous consensus recommendations, the use of the Mayo Clinic Classification for prognostication purposes has been validated in patients with class 1 ADPKD. Recent evidence supports the benefits of a low-osmolar diet and dietary sodium restriction in patients with ADPKD. Evidence from the Replicating Evidence of Preserved Renal Function: an Investigation of Tolvaptan Safety and Efficacy in ADPKD (REPRISE) trial supports the use of ADH (antidiuretic hormone) receptor antagonism in patients with ADPKD 18 to 55 years of age with eGFR (estimated glomerular filtration rate) of 25 to 65 mL/min/1.73 m 2 or 56 to 65 years of age with eGFR of 25 to 44 mL/min/1.73 m 2 with historical evidence of a decline in eGFR >2.0 mL/min/1.73 m 2 /year. Limitations: Available literature was limited to English language publications and to publications indexed in PubMed, the Cochrane Library, and Google Scholar. Implications: Advances in the assessment of the risk of disease progression include the validation of the Mayo Clinic Classification for patients with class 1 ADPKD. Advances in the pharmacological management of ADPKD include the expansion of the use of ADH receptor antagonism in patients 18 to 55 years of age with eGFR of 25 to 65 mL/min/1.73 m 2 or 56 to 65 years of age with eGFR of 25 to 44 mL/min/1.73 m 2 with historical evidence of a decline in eGFR >2.0 mL/min/1.73 m 2 /year, as per the results of the REPRISE study.

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)0.047
metaresearch head score (Gemma)0.097
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: Methods · Consensus signal: none
Teacher disagreement score0.255
Threshold uncertainty score0.506

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.097
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0100.007
Science and technology studies0.0030.002
Scholarly communication0.0050.003
Open science0.0070.004
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0060.002

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 head0.013
GPT teacher head0.304
Teacher spread0.292 · 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
GenreMethods

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

Citations34
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Kidney Health and DiseaseSame topicGenetic and Kidney Cyst DiseasesFrench-language works237,207