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Record W3152931546 · doi:10.1016/j.ekir.2021.03.338

POS-322 INSIDE CKD: PROJECTING THE FUTURE BURDEN OF CHRONIC KIDNEY DISEASE IN THE AMERICAS AND THE ASIA-PACIFIC REGION USING MICROSIMULATION MODELLING

2021· article· en· W3152931546 on OpenAlexaffabout
Juan José García Sánchez, Navdeep Tangri, A. Abdul Sultan, M.C. Batista, Claudia Cabrera, Steven J. Chadban, Glenn M. Chertow, Eiichiro Kanda, G. Li, S. Nolan, Lise Retat, Song Xin, Laura Webber, Jay B. Wish, Mingyue Xu

Bibliographic record

VenueKidney International Reports · 2021
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsUniversity of ManitobaOrthopaedic Innovation Centre
Fundersnot available
KeywordsKidney diseaseMicrosimulationMedicinePublic healthDisease burdenDiseaseEnvironmental healthGerontologyIntensive care medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) is a debilitating and costly condition, affecting about 10% of people globally. In the past decade, the increasing prevalence of CKD has been linked to rising rates of cardiovascular disease events, adverse renal outcomes and mortality. The future trajectories of CKD prevalence, progression and outcomes, as well as related costs, are critical considerations for public health and policy planning. Inside CKD aims to project, for the 2020–2025 period, the public health burden of CKD in Canada using a patient-level microsimulation-based model.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.505

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.070
GPT teacher head0.353
Teacher spread0.283 · 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 designSimulation or modeling
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

Citations2
Published2021
Admission routes2
Has abstractyes

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