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Record W2967620808 · doi:10.1136/bmjopen-2019-030048

Risk factors for contrast-induced acute kidney injury (CI-AKI): protocol for systematic review and meta-analysis

2019· article· en· W2967620808 on OpenAlexaboutno aff
Yong Liu, Xingcheng Liang, Shaojun Xin, Jin Liu, Guoli Sun, Shiqun Chen, Xiaolin Cen, Xiaohua Dai, Yibo He, Feier Song, Yan Liang, Y. Hu, Yingling Zhou, Zhujun Chen, Ning Tan, Jiyan Chen

Bibliographic record

VenueBMJ Open · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
FundersScience and Technology Planning Project of Guangdong ProvinceNational Science Foundation
KeywordsMedicineProtocol (science)Meta-analysisMEDLINESystematic reviewAcute kidney injuryIntensive care medicinePublication biasCochrane collaborationInternal medicineAlternative medicinePathologyCochrane Library

Abstract

fetched live from OpenAlex

INTRODUCTION: Identifying the patients who are at risk for contrast-induced acute kidney injury (CI-AKI), which is defined as an increase in serum creatinine after exposure to contrast media, is a critical step in targeted prevention strategies. The absolute and relative importance of individual risk factors have not been systematically evaluated, let alone the new, controversial and modifiable risk factors of CI-AKI. METHODS AND ANALYSIS: On 1 July 2019, a search was performed on MEDLINE, Embase and the Cochrane Database of Systematic Reviews. We will perform a systematic review and meta-analysis to assess the important risk factors for developing CI-AKI, including those new, modifiable factors, which are considered controversial. The secondary endpoint will be all-cause mortality. Two authors will then independently screen studies that meet the criteria for inclusion, consulting with a third author to resolve any dispute. The quality of the included studies will be assessed according to the Newcastle-Ottawa scale. ETHICS AND DISSEMINATION: Ethics approval in this systematic review and meta-analysis protocol is not needed. We will disseminate the findings of this systematic review and meta-analysis via publications in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42019121534.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.650
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.199
GPT teacher head0.518
Teacher spread0.318 · 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 designMeta-analysis
Domainnot available
GenreProtocol

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

Citations26
Published2019
Admission routes1
Has abstractyes

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