Tailored hydration for the prevention of contrast-induced acute kidney injury after coronary angiogram or PCI: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Contrast-induced acute kidney injury (CI-AKI) is a frequent complication of coronary interventions associated with an increased risk of mortality and morbidity. The optimal intravenous hydration strategy to prevent CI-AKI is not well-established. The primary objective is to determine if a tailored hydration strategy reduces the risk of CI-AKI and of major adverse cardiovascular events (MACE) in patients undergoing coronary angiography compared with a nontailored hydration strategy. METHODS: A study-level meta-analysis of randomized controlled trials comparing tailored versus nontailored hydration strategies for the prevention of CI-AKI (primary outcome) and of MACE (main secondary outcome) in patients undergoing coronary angiography for any indication was performed. Tailored hydration was defined as the administration of intravenous fluids based on patient-specific parameters other than weight only. RESULTS: = 0%). The impact of a tailored strategy on the primary outcome was consistent in sensitivity analyses. CONCLUSION: These results suggest that tailored hydration is superior to nontailored hydration in reducing the risk of CI-AKI and MACE in patients undergoing coronary angiography. Future trials are required to identify the optimal tailored hydration strategy.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".