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Record W3202496852 · doi:10.34172/ps.2021.15

Sodium Bicarbonate versus Statins to Prevent Contrast-induced Acute Kidney Injury: A Comprehensive Review

2021· review· en· W3202496852 on OpenAlexaff
Sanam Dolati, Ata Mahmoodpoor, Nafiseh Gharizadeh, Saina Gholipouri, Hassan Soleimanpour

Bibliographic record

VenuePharmaceutical Sciences · 2021
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineSodium bicarbonateAcute kidney injuryContrast-induced nephropathyAcute tubular necrosisIodinated contrastAtorvastatinIsotonicIntensive care medicineRenal functionUrologyInternal medicineNephropathySurgeryDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

Iodinated contrast agents are commonly used in diagnostic radiography techniques along with therapeutic interventions. Contrast-Induced Acute Kidney Injury (CI-AKI) is a significant problem of all angiographic procedures, triggered by the use of Iodinated Contrast Media (ICM). There are conflicting data concerning the prevention and treatment of CI-AKI. Numerous approaches have been studied to prevent CI-AKI, but the therapy of choice remains undetermined. The cornerstones of CI-AKI prevention include low-osmolar ICM and intravenous hydration. The recommended hydration must be achieved by means of an isotonic solution of saline. Statins were tested against AKI due to their anti-inflammatory action and antioxidant effect on endothelial function. Novel approaches are required to investigate the short-term effects of high dosage atorvastatin versus sodium bicarbonate on CI-AKI prevention. The objective of this review is to compare the findings of various studies that had applied different doses of statins, sodium bicarbonate, and other agents for preventing CI-AKI.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.281
GPT teacher head0.546
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2021
Admission routes1
Has abstractyes

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