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Record W2160916769 · doi:10.1155/2013/467326

Bicarbonates for the Prevention of Postoperative Renal Failure in Endovascular Aortic Aneurysm Repair: A Randomized Pilot Trial

2013· article· en· W2160916769 on OpenAlexaff
Véronique Brulotte, Stéphane Elkouri, Éric Thérasse, Vincent Pichette, Pierre Beaulieu

Bibliographic record

VenueAnesthesiology Research and Practice · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineAcute kidney injuryLipocalinUrologyAlgorithmSurgeryInternal medicineComputer science

Abstract

fetched live from OpenAlex

Purpose. Contrast-induced nephropathy (CIN) can contribute to acute kidney injury (AKI) in patients undergoing endovascular aortic aneurysm surgery. We evaluated the incidence of AKI together with the evolution of early biomarkers of renal injury in patients receiving bicarbonates or NaCl 0.9%. Methods. This study involved endovascular aortic aneurysm surgery patients. Group A (n = 17) received bicarbonates 3 mL/kg/h for 1 h before the procedure and then 1 mL/kg/h until 6 h after surgery, whereas group B (n = 17) received NaCl 0.9% using the same protocol. Biomarkers of renal injury from urine (interleukin-18 (IL-18), neutrophil gelatinase-associated lipocalin (NGAL), N-acetyl-β-D-glucosaminidase (NAG), and kidney injury molecule 1 (KIM-1)) and blood (NGAL, cystatin C) were measured at baseline and 3, 24, and 48 h postoperatively. Results. AKI occurred in 1 patient (2.9%), in the bicarbonates group. IL-18, NAG, NGAL, and KIM-1 significantly rose in both groups after the surgery. There was a greater rise in NGAL and IL-18 after 3 h in the bicarbonates versus NaCl 0.9% group: 1115% versus 240% increase (P = 0.03) and 338% increase versus 1.4% decrease (P = 0.01). Conclusions. Despite significant elevation in biomarkers of renal injury, we demonstrated a low rate of AKI following endovascular aortic surgery.

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)0.012
metaresearch head score (Gemma)0.059
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.087
GPT teacher head0.412
Teacher spread0.325 · 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.

Study designRandomized trial
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

Citations18
Published2013
Admission routes1
Has abstractyes

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