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Record W2306902418 · doi:10.1093/ndt/gfv197.24

SP557SYSTEMATIC REVIEW OF THE INCIDENCE OF SUDDEN CARDIAC DEATH IN ADULTS WITH END-STAGE RENAL DISEASE

2015· article· en· W2306902418 on OpenAlexaff
Sharanya Ramesh, Ann A. Zalucky, Derek J. Roberts, Sofia B. Ahmed, Stephen B. Wilton, Min Jun

Bibliographic record

VenueNephrology Dialysis Transplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineEnd stage renal diseaseIncidence (geometry)DiseaseStage (stratigraphy)Sudden cardiac deathCardiologyInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Introduction and Aims: Although sudden cardiac death (SCD) is now recognized as a distinct cause of death in patients with end stage renal disease (ESRD), its incidence has not been well summarized. We aimed to systematically review the totality of evidence regarding the incidence of SCD among adults with ESRD on dialysis. Methods: We performed a systematic review and meta-analysis of the literature based on a protocol developed a priori. We searched MEDLINE and EMBASE (inception to October 2013) for cohort studies reporting the incidence of SCD in adult patients with ESRD on hemo- or peritoneal dialysis. We collected data on number of SCD as well as the definition of SCD for each individual study. We used a random-effects model to summarize the incidence of SCD. We conducted subgroup analyses to explore for source of heterogeneity. Results: Twenty one studies (n=56,790 patients) were included in the meta-analysis. The incidence of SCD among adults with ESRD ranged from 0.5 to 6.67 deaths per 100 person-years. The definitions and assessment of SCD varied across the included studies. There was evidence of significant heterogeneity (I2=98.2; p<0.001), which was not explained by subgroup analyses stratified by mean age, proportion of hypertensive or diabetic patients, follow-up time, study size, or type of cohort studied (population-based versus not).

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.019
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.251
Teacher spread0.239 · 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.

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
Published2015
Admission routes1
Has abstractyes

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