MétaCan
Menu
Back to cohort
Record W1982627981 · doi:10.1155/2012/435736

Modifiable Risk Factors for Early Mortality on Hemodialysis

2012· article· en· W1982627981 on OpenAlexaffabout
Rory McQuillan, Lilyanna Trpeski, Stanley Fenton, Charmaine E. Lok

Bibliographic record

VenueInternational Journal of Nephrology · 2012
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineDialysisHemodialysisHazard ratioProportional hazards modelMalnutritionInternal medicineCentral venous catheterRisk factorBody mass indexIntensive care medicineEmergency medicinePediatricsSurgeryCatheterConfidence interval

Abstract

fetched live from OpenAlex

Data of incident hemodialysis patients from 2001 to 2007 were abstracted from The Renal Disease Registry (TRDR) from central Ontario, Canada and followed until December 2008 to determine 90-day mortality rates for incident hemodialysis patients. Modifiable risk factors of early mortality were determined by a Cox model. In total, 876 of 4807 incident patients died during their first year on dialysis; 304 (34.7%) deaths occurred within the first 90 days of dialysis initiation. The majority of deaths were attributed to a cardiovascular event or infection and more likely occurred in older patients and those with cardiovascular co-morbidities. Of potentially modifiable risk factors, low body mass index (<18.5), a surrogate for malnutrition, was a strong predictor of early mortality [adjusted hazard ratio (HR) 4.22 (CI: 3.12-5.17)]. Also, central venous catheter use was associated with a 2.40 fold increase risk of death (CI: 1.4-3.90). Patients who attended a multidisciplinary pre-dialysis clinic were less likely to die (HR: 0.60, CI: 0.47-0.78). The first 90 days after initiation of dialysis is a period of especially high risk of death. We have identified potentially modifiable risk factors in vascular access type, pre-dialysis care and nutritional status.

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 score0.033
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.034
GPT teacher head0.324
Teacher spread0.290 · 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

Citations50
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of NephrologySame topicDialysis and Renal Disease ManagementFrench-language works237,207