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Record W2040483791 · doi:10.1093/ndt/16.suppl_2.7

Prevalence of cardiovascular damage in early renal disease

2001· review· en· W2040483791 on OpenAlexaffabout
Anna S. Levin

Bibliographic record

VenueNephrology Dialysis Transplantation · 2001
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineLeft ventricular hypertrophyInternal medicineBlood pressureCardiologyKidney diseaseDiseaseDialysisRenal functionEnd stage renal diseaseRisk factorProspective cohort study

Abstract

fetched live from OpenAlex

There is a large burden of cardiovascular disease in early renal disease due to multiple risk factors. Although left ventricular hypertrophy (LVH) is prevalent early in the process of progressive renal decline, it is associated with a number of modifiable risk factors (e.g. anaemia and systolic blood pressure (BP)). More importantly, treatment of modifiable risk factors in renal disease can delay progression. It is important to define anaemia physiologically and to remember that it is also associated with a number of cardiovascular risk factors that may/may not be independent of each other. In a recent prospective, multicentre Canadian study of early renal disease patients prior to dialysis (n=446), the baseline prevalence of LVH increased both with decreasing renal function and decreasing haemoglobin (Hb) levels. Notably, Hb levels within current guideline target levels were still associated with a very high degree of LVH. Over a 12-month period, only a decrease in Hb and an increase in systolic BP, and baseline left ventricular mass index (LVMI) predicted left ventricular growth. Patients whose cardiac symptoms progressed over 12 months were those who experienced a significant fall in BP and a significant increase in LVMI during that time. In the future, steps are needed to ensure early identification of both renal disease and specific risk factors. Recognizing modifiable risk factors and addressing them early in the course of renal disease will facilitate the improvement of patient outcomes.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.282
Teacher spread0.261 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations45
Published2001
Admission routes2
Has abstractyes

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