Lung–Kidney Cross-Talk in the Critically Ill Patient
Bibliographic record
Abstract
Discoveries have emerged highlighting the complex nature of the interorgan cross-talk between the kidney and the lung. Vascular rigidity, neurohormonal activation, tissue hypoxia, and abnormal immune cell signaling have been identified as common pathways leading to the development and progression of chronic kidney disease. However, our understanding of the causal relationships between lung injury and kidney injury is not precise. This review discusses a number of features and mechanisms of renal dysfunction in pulmonary disorders in relation to respiratory acidosis, impaired gas exchange, systemic congestion, respiratory support/replacement therapies, and other issues relevant to the clinical care of these patients. Biotrauma due to injurious ventilatory strategies can lead to the release of mediators into the lung, which may then translocate into the systemic circulation and cause end-organ dysfunction, including renal dysfunction. Right ventricular dysfunction and congestive states may contribute to alterations of renal perfusion and oxygenation, leading to diuretic resistance and recurrent hospitalization. In patients with concomitant respiratory failure, noninvasive ventilation represents a promising treatment option for the correction of impaired renal microcirculation and endothelial dysfunction. In patients requiring extracorporeal membrane oxygenation, short- and long-term monitoring of kidney function is warranted, as they are at highest risk of developing acute kidney injury and fluid overload.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".