Risk factors for contrast-induced acute kidney injury (CI-AKI): protocol for systematic review and meta-analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.114 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".