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Record W4392112769 · doi:10.15173/m.v1i44.3620

Chronic Kidney Disease

2024· article· en· W4392112769 on OpenAlexvenueaboutno aff
Jacqueline Chen, Ria Patel

Bibliographic record

VenueThe Meducator · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsnot available
Fundersnot available
KeywordsKidney diseaseDiseaseMedicinePeer reviewIntensive care medicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) is defined as low filtration function in the kidneys, protein in the urine, or functionally-important structural abnormalities. Low filtration function is associated with waste build-up in the bloodstream and difficulty in excreting salt and water, leading to fluid build-up. CKD may worsen over time, and some individuals with CKD may experience kidney failure, at which point dialysis or transplantation is required to replace kidney function. CKD may be caused by diabetes, renovascular disease, glomerulonephritis, polycystic kidney disease, and various genetic and environmental factors. In 2023, approximately four million Canadians live with and 11-13% of the global population are affected by CKD. This disorder has a 50% five-year mortality rate and is associated with lower quality of life compared to other chronic diseases, including sickle cell anemia, cancer, and cystic fibrosis. CKD’s comorbidities, such as diabetes, are increasing in prevalence and can increase the risk of developing CKD. Thus, the rates of CKD are also set to rise.3 Annual CKD management costs across Canada total $40 billion, with dialysis treatment for people with end-stage kidney disease (ESKD) costing $100,000 per patient.

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)0.008
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: Empirical · Consensus signal: none
Teacher disagreement score0.101
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1010.022

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 head0.012
GPT teacher head0.292
Teacher spread0.280 · 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
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
Published2024
Admission routes2
Has abstractyes

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