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Proton pump inhibitors use and risk of chronic kidney disease and end-stage renal disease

2021· article· en· W3137753146 on OpenAlexaboutno aff
Carolina Santos Vengrus, Vinícius Daher Alvares Delfino, Paulo Roberto Bignardi

Bibliographic record

VenueMinerva Urology and Nephrology · 2021
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKidney diseaseEnd stage renal diseaseRelative riskInternal medicineCochrane LibraryConfidence intervalMeta-analysisObservational studyMedical prescriptionDiseaseIntensive care medicinePharmacology

Abstract

fetched live from OpenAlex

INTRODUCTION: A possible association between long-term proton pump inhibitors (PPI) use and chronic kidney disease (CKD) has been recently described. Due to the potential health risk of this association, in the absence of proper clinical trials, we have decided to carry out a systematic review followed by meta-analysis. EVIDENCE ACQUISITION: PubMed, Cochrane Library, and Lilacs databases were searched. Studies that reported an association between PPI use and CKD or End-stage Renal Disease (ESRD) published until December 23, 2019, were included. All selected studies present high quality according to the New-Castle-Ottawa. The risk ratio (RR) and confidence interval (CI) were pooled using a random-effects model in CKD outcome analysis and fixed effects model for ESRD. A total of 10 observational studies were selected. EVIDENCE SYNTHESIS: Compared to patients who did not use PPI, the RR for CKD associated with PPI use was 1.35 (95% CI 1.15-1.56) with P<0.001, and the RR for ESRD associated with PPI use was 1.49 (95% CI 1.41-1.56) with P<0.001. CONCLUSIONS: This study indicates the presence of a significant association between PPI use and an increased risk of CKD and ESRD and reiterates the need for the medical prescription of this class of drugs to be made following the guidelines of the FDA.

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 score0.007
Threshold uncertainty score0.696

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 studies0.0000.001
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 head0.012
GPT teacher head0.248
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

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

Citations17
Published2021
Admission routes1
Has abstractyes

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