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Record W2784134840 · doi:10.1177/2054358117749530

Canadians Seeking Solutions and Innovations to Overcome Chronic Kidney Disease (Can-SOLVE CKD): Form and Function

2018· article· en· W2784134840 on OpenAlexafffundabout
Adeera Levin, E. D. Adams, Brendan J. Barrett, Heather Beanlands, Kevin D. Burns, Helen Chiu, Kate Chong, Allison Dart, Jack Ferera, Nicolás Fernández, Elisabeth Fowler, Amit X. Garg, Richard E. Gilbert, Heather Harris, Rebecca Harvey, Brenda R. Hemmelgarn, Matthew T. James, Jeffrey Johnson, Joanne Kappel, Paul Komenda, Michael McCormick, Christopher W. McIntyre, Farid H. Mahmud, York Pei, Graham Pollock, Heather N. Reich, Norman D. Rosenblum, James W. Scholey, Etienne Sochett, Mila Tang, Navdeep Tangri, Marcello Tonelli, C. Turner, Michael Walsh, Cathy Woods, Braden Manns

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsToronto General HospitalLawson Health Research InstituteSeven Oaks General HospitalUniversity Health NetworkUniversity of AlbertaUniversity of CalgaryOntario Stroke NetworkSt. Michael's HospitalMemorial University of NewfoundlandUniversity of TorontoPopulation Health Research InstituteWestern UniversityHospital for Sick ChildrenProvidence Health Care Research InstituteChildren's Hospital Research Institute of ManitobaUniversity of SaskatchewanUniversité de MontréalUniversity of British ColumbiaFoothills Medical CentreKidney Foundation of CanadaInstitute for Clinical Evaluative SciencesUniversity of OttawaUniversity of ManitobaOttawa HospitalMcMaster UniversityToronto Metropolitan University
FundersBreakthrough T1D CanadaCanadian Institutes of Health ResearchHealth Canada
KeywordsKidney diseaseMedicineRenal functionIntensive care medicineFunction (biology)DiseaseEnvironmental healthInternal medicineBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article serves to describe the Can-SOLVE CKD network, a program of research projects and infrastructure that has excited patients and given them hope that we can truly transform the care they receive. ISSUE: Chronic kidney disease (CKD) is a complex disorder that affects more than 4 million Canadians and costs the Canadian health care system more than $40 billion per year. The evidence base for guiding care in CKD is small, and even in areas where evidence exists, uptake of evidence into clinical practice has been slow. Compounding these complexities are the variations in outcomes for patients with CKD and difficulties predicting who is most likely to develop complications over time. Clearly these gaps in our knowledge and understanding of CKD need to be filled, but the current state of CKD research is not where it needs to be. A culture of clinical trials and inquiry into the disease is lacking, and much of the existing evidence base addresses the concerns of the researchers but not necessarily those of the patients. PROGRAM OVERVIEW: The Canadian Institutes of Health Research (CIHR) has launched the national Strategy for Patient-Oriented Research (SPOR), a coalition of federal, provincial, and territorial partners dedicated to integrating research into care. Canadians Seeking Solutions and Innovations to Overcome Chronic Kidney Disease (Can-SOLVE CKD) is one of five pan-Canadian chronic kidney disease networks supported through the SPOR. The vision of Can-SOLVE CKD is that by 2020 every Canadian with or at high risk for CKD will receive the best recommended care, experience optimal outcomes, and have the opportunity to participate in studies with novel therapies, regardless of age, sex, gender, location, or ethnicity. PROGRAM OBJECTIVE: The overarching objective of Can-SOLVE CKD is to accelerate the translation of knowledge about CKD into clinical research and practice. By focusing on the patient's voice and implementing relevant findings in real time, Can-SOLVE CKD will transform the care that CKD patients receive, and will improve kidney health for future generations.

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)0.000
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.458
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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.

Study designNot applicable
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

Citations74
Published2018
Admission routes3
Has abstractyes

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