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. 2025 Sep 1;53(9):e1849-e1850.
doi: 10.1097/CCM.0000000000006762. Epub 2025 Sep 2.

The authors reply

Elizabeth Levy  1 , Gary E Weissman  2 , Meeta Prasad Kerlin  2 , Michael W Sjoding  3
Affiliations

The authors reply

Elizabeth Levy et al. Crit Care Med. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

Drs. Levy, Kerlin, and Sjoding received support for article research from the National Institutes of Health. Dr. Kerlin’s institution received funding from the National Heart, Lung, and Blood Institute. Dr. Weissman has disclosed that he does not have any potential conflicts of interest.

References

    1. Ye K: Critical analysis of temporal resolution and diagnostic labeling in acute respiratory distress syndrome detection models. Crit Care Med 2025; 53:e1847–e1848
    1. Levy E, Claar D, Co I, et al.: Development and external validation of a detection model to retrospectively identify patients with acute respiratory distress syndrome. Crit Care Med 2025; 53:e1224–e1234
    1. Reamaroon N, Sjoding MW, Lin K, et al.: Accounting for label uncertainty in machine learning for detection of acute respiratory distress syndrome. IEEE J Biomed Health Inform 2018; 23:407–415
    1. Sjoding MW, Hofer TP, Co I, et al.: Interobserver reliability of the berlin ARDS definition and strategies to improve the reliability of ARDS diagnosis. Chest 2018; 153:361–367
    1. Hochberg CH, Psoter KJ, Eakin MN, et al.: Declining use of prone positioning after high initial uptake in COVID-19 adult respiratory distress syndrome. Crit Care Med 2023; 51:1547–1551

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