Lung–kidney interactions in critically ill patients: consensus report of the Acute Disease Quality Initiative (ADQI) 21 Workgroup
Bibliographic record
Abstract
BACKGROUND: Multi-organ dysfunction in critical illness is common and frequently involves the lungs and kidneys, often requiring organ support such as invasive mechanical ventilation (IMV), renal replacement therapy (RRT) and/or extracorporeal membrane oxygenation (ECMO). METHODS: A consensus conference on the spectrum of lung-kidney interactions in critical illness was held under the auspices of the Acute Disease Quality Initiative (ADQI) in Innsbruck, Austria, in June 2018. Through review and critical appraisal of the available evidence, the current state of research, and both clinical and research recommendations were described on the following topics: epidemiology, pathophysiology and strategies to mitigate pulmonary dysfunction among patients with acute kidney injury and/or kidney dysfunction among patients with acute respiratory failure/acute respiratory distress syndrome. Furthermore, emphasis was put on patients receiving organ support (RRT, IMV and/or ECMO) and its impact on lung and kidney function. CONCLUSION: The ADQI 21 conference found significant knowledge gaps about organ crosstalk between lung and kidney and its relevance for critically ill patients. Lung protective ventilation, conservative fluid management and early recognition and treatment of pulmonary infections were the only clinical recommendations with higher quality of evidence. Recommendations for research were formulated, targeting lung-kidney interactions to improve care processes and outcomes in critical illness.
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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 | 0.078 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".