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Record W4324134550 · doi:10.1093/ckj/sfad034

Association of serum bicarbonate with the development of kidney stones in patients with chronic kidney disease: a retrospective cohort study

2023· article· en· W4324134550 on OpenAlexaff
Navdeep Tangri, Vandana Mathur, Nancy L. Reaven, Susan E. Funk, Reid Whitlock, Donald E. Wesson, David A. Bushinsky

Bibliographic record

VenueClinical Kidney Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsSeven Oaks General HospitalUniversity of Manitoba
Fundersnot available
KeywordsMedicineMetabolic acidosisKidney diseaseInternal medicineAcidosisKidney stonesBicarbonateHazard ratioCohortDialysisRisk factorEndocrinologyGastroenterologyConfidence interval

Abstract

fetched live from OpenAlex

Background: Epidemiological studies demonstrate an association between kidney stones and risk of chronic kidney disease (CKD) and CKD progression. Metabolic acidosis, as a consequence of CKD, results in a reduced urine pH which promotes the formation of some types of kidney stones and inhibits the formation of others. While metabolic acidosis is a risk factor for CKD progression, the association of serum bicarbonate with risk of incident kidney stones is not well understood. Methods: We used an Integrated Claims-Clinical dataset of US patients to generate a cohort of patients with non-dialysis-dependent CKD with two serum bicarbonate values of 12 to <22 mmol/L (metabolic acidosis) or 22 to <30 mmol/L (normal serum bicarbonate). Primary exposure variables were baseline serum bicarbonate and change in serum bicarbonate over time. Cox proportional hazards models evaluated time to first occurrence of kidney stones during a median 3.2-year follow-up. Results: < .0001). Both lower baseline serum bicarbonate [hazard ratio (HR) 1.047; 95% confidence interval (CI) 1.036-1.057] and decreasing serum bicarbonate over time (HR 1.034; 95% CI 1.026-1.043) were associated with increased risk of kidney stone development. Conclusions: Metabolic acidosis was associated with a higher incidence of kidney stones and shorter time to incident stone formation in patients with CKD. Future studies may investigate the role of correcting metabolic acidosis to prevent stone formation.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.015
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.309
Teacher spread0.294 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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