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Record W6940054631 · doi:10.6084/m9.figshare.c.5803448

Association between exercise frequency with renal and cardiovascular outcomes in diabetic and non-diabetic individuals at high cardiovascular risk

2022· other· en· W6940054631 on OpenAlexaff

Bibliographic record

VenueFigshare · 2022
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsAlbuminuriaDiabetes mellitusKidney diseaseProportional hazards modelIncidence (geometry)Physical activityDiseaseType 2 diabetesAnalysis of variance

Abstract

fetched live from OpenAlex

Abstract Background Guidelines recommend physical activity to reduce cardiovascular (CV) events. The association between physical activity and progression of chronic kidney disease (CKD) with and without diabetes is unknown. We assessed the association of self-reported physical activity with renal and CV outcomes in high-risk patients aged ≥ 55 years over a median follow-up of 56 months in post-hoc analysis of a previously randomized trial program. Methods Analyses were done with Cox regression analysis, mixed models for repeated measures, ANOVA and χ2-test. 31,312 patients, among them 19,664 with and 11,648 without diabetes were analyzed. Results Physical activity was inversely associated with renal outcomes (doubling of creatinine, end-stage kidney disease (ESRD)) and CV outcomes (CV death, myocardial infarction, stroke, heart failure hospitalization). Moderate activity (at least 2 times/week to every day) was associated with lower risk of renal outcomes and lower incidence of new albuminuria (p

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.180
Teacher spread0.171 · 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 designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

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