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Record W2390742373 · doi:10.1093/joneph/23.1.33

Dialysate potassium and risk of death in chronic hemodialysis patients

2009· article· en· W2390742373 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Nephrology · 2009
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsInstitute of Health EconomicsProvincial Laboratory of Public HealthSouth Health CampusUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineHemodialysisHazard ratioConfidence intervalInternal medicineDialysisHyperkalemiaProportional hazards modelCohortGastroenterology

Abstract

fetched live from OpenAlex

BACKGROUND: Few data guide the prescription of dialysate potassium (dK) in hemodialysis, which is usually prescribed empirically on the basis of predialysis serum potassium levels. METHODS: This was a retrospective cohort study of prospectively collected data. We studied all patients initiating chronic hemodialysis in the Northern Alberta Renal Program (NARP) between January 2001 and December 2006. Data on demographic, clinical and treatment characteristics as well as the dates of death or transplant were extracted from the NARP database. We aimed to examine the relation between dialysate potassium level and all-cause death. RESULTS: During the study, 515/1,267 of patients (41%) died. The frequency of dK of 0 or 1 mEq/L, 2, 3 and 4 mEq/L was 6%, 40%, 51% and 3%, respectively. In our base model, which considered dK as a categorical exposure, the hazard ratios associated with 0 or 1 mEq/L, 2, 3 and 4 mEq/L were 1.13 (95% confidence interval [95% CI], 0.78-1.63), 1 (referent), 1.29 (95% CI, 1.07-1.56) and 1.74 (95% CI, 1.09-2.77), respectively. When markers of inflammation or malnutrition were adjusted for separately, the association between dK and mortality was attenuated but remained significant. After simultaneous adjustment for markers of inflammation and malnutrition, the risk of death associated with the higher dK categories was attenuated, and the overall trend was eliminated. Analyses using dK as a time-varying covariate found similar results. CONCLUSIONS: Although unadjusted and partially adjusted models suggested a graded association between higher dK and the risk of all-cause death, this association was apparently due to confounding by factors suggesting malnutrition and inflammation. The relative paucity of data on the association between dK and clinical outcomes despite the biological importance of potassium suggest that further studies are needed.

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.

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 score0.041
Threshold uncertainty score0.273

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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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