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Record W2103846010 · doi:10.1681/asn.2010050442

Statin Use Associates with a Lower Incidence of Acute Kidney Injury after Major Elective Surgery

2011· article· en· W2103846010 on OpenAlexafffundabout
Amber O. Molnar, Steven G. Coca, Arsh K. Jain, Abhijat Kitchlu, Jin Jun Luo, Chirag R. Parikh, J. Michael Paterson, Nausheen Siddiqui, Ron Wald, Michael Walsh, Amit X. Garg

Bibliographic record

VenueJournal of the American Society of Nephrology · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of TorontoMcMaster UniversityInstitute for Clinical Evaluative SciencesWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicineAcute kidney injuryDialysisPerioperativeOdds ratioStatinPropensity score matchingRetrospective cohort studySurgeryElective surgeryPopulationKidney diseaseIncidence (geometry)Mortality rateInternal medicine

Abstract

fetched live from OpenAlex

Statins abrogate ischemic renal injury in animal studies but whether they are renoprotective in humans is unknown. We conducted a population-based retrospective cohort study that included 213,347 older patients who underwent major elective surgery in the province of Ontario, Canada from 1995 to 2008. During the first 14 postoperative days, 1.9% (4020 patients), developed acute kidney injury and 0.5% (1173 patients), required acute dialysis. The 30-day mortality rate was 2.8% (5974 patients). Prior to surgery, 32% of patients were taking a statin. After statistical adjustment for patient and surgical characteristics, statin use associated with 16% lower odds of acute kidney injury (OR, 0.84; 95% CI, 0.79 to 0.90), 17% lower odds of acute dialysis (OR, 0.83; 95% CI, 0.72 to 0.95), and 21% lower odds of mortality (OR, 0.79; 95% CI, 0.74 to 0.85). Propensity score matching produced similar results. These data suggest that statins may protect against renal complications after major elective surgery and reduce perioperative mortality.

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)0.001
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 score0.038
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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

Citations122
Published2011
Admission routes3
Has abstractyes

Explore more

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