Inhaled nitric oxide and acute kidney injury: new insights from observational data
Bibliographic record
Abstract
The discovery of nitric oxide [1] generated excitement among intensivists because, as a therapeutic gas, it improved perfusion to ventilated lung units and increased arterial oxygenation without obvious systemic effects.Subsequent randomised trials in patients with acute respiratory distress syndrome (ARDS) found shortterm improvements in oxygenation, with no effect on mortality and an unexpected increased risk of acute kidney injury (AKI) [2,3].In the absence of compelling biological mechanisms, one explanation could be that nitric oxide was used harmfully in trial protocols as opposed to clinician-directed practice.Although observational investigations can address this hypothesis, they are prone to bias and confounding that persist despite efforts at statistical 'control' during study design or analysis.However, empirical comparisons of treatment effects in randomised trials and observational studies have yielded mixed results [4,5], and design alone does not determine the truth of study findings.Ruan and colleagues recently published a retrospective cohort study (n = 547; 2007-2015) evaluating the relationship between inhaled nitric oxide administered in the first 3 days of ARDS and subsequent need for renal replacement therapy (RRT) [6].They found that nitric oxide was associated with a substantial increase in RRT (adjusted hazard ratio 1.59, 95% confidence interval 1.08-2.34),consistent with meta-analyses of trials [2,3].
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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 | 0.034 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".