Acute Kidney Injury following Unselected Emergency Admission: Role of the Inflammatory Response, Medication and Co-Morbidity
Bibliographic record
Abstract
BACKGROUND/AIMS: Acute kidney injury (AKI) following admission to hospital is associated with increased mortality, morbidity and length of stay. Factors that predispose patients to AKI frequently co-exist. The precise description of their representation in unselected admissions could help define mechanistic inter-relationships and optimise risk stratification strategies. Our aim was therefore to define precisely, using electronically available data, the variables that are associated with AKI. METHODS: A cohort study of 112,987 emergency admissions to an urban academic medical centre between 2006 and 2010 was performed. Post-admission AKI was defined using KDIGO aligned, proportionate changes in serum creatinine, denominated by the first measured. AKI correlated with co-morbidities, medications received and the C-reactive protein concentration (CRP). RESULTS: The relationship between post-admission AKI and putative risk factors was defined in univariate and multivariate analyses. Inclusion of CRP in multivariate analyses significantly reduced the strength of association between some co-variables such as radiological contrast and gentamicin administration but not others. CONCLUSION: The effect of CRP in these analyses supports the role of systemic inflammation in susceptibility to post-admission AKI. It accounts for the greater part of univariate associations between AKI and some nephrotoxic agents, placing the risk attributable to their use in context. Quantification of the systemic inflammatory response may have utility in AKI risk stratification, integrating various determinants of susceptibility.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".