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VOLUME 28 , ISSUE 2 ( February, 2024 ) > List of Articles


Author Response

Zahra Rahmatinejad, Benyamin Hoseini, Ali Pourmand, Hamidreza Reihani, Fatemeh Rahmatinejad, Saeid Eslami, Ameen Abu Hanna

Keywords : COVID-19, Emergency department, Mortality prediction, Performance measures, Scoring systems

Citation Information : Rahmatinejad Z, Hoseini B, Pourmand A, Reihani H, Rahmatinejad F, Eslami S, Hanna AA. Author Response. Indian J Crit Care Med 2024; 28 (2):183-184.

DOI: 10.5005/jp-journals-10071-24609

License: CC BY-NC 4.0

Published Online: 30-01-2024

Copyright Statement:  Copyright © 2024; The Author(s).


  1. Finsterer J, Scorza FA. Calibration of prediction models of in-hospital mortality in SARS-CoV-2 patients depends also on data quality. Indian J Crit Care Med 2024;28(2):181–182.
  2. Schomaker M, Heumann C. Bootstrap inference when using multiple imputation. Stat Med 2018;37(14):2252–2266. DOI: 10.1002/sim.7654.
  3. Rahmatinejad Z, Hoseini B, Rahmatinejad F, Abu-Hanna A, Bergquist R, Pourmand A, et al. Internal Validation of the predictive performance of models based on three ED and ICU scoring systems to predict inhospital mortality for intensive care patients referred from the emergency department. Biomed Res Int 2022;2022:3964063. DOI: 10.1155/2022/3964063.
  4. Rahmatinejad Z, Tohidinezhad F, Rahmatinejad F, Eslami S, Pourmand A, Abu-Hanna A, et al. Internal validation and comparison of the prognostic performance of models based on six emergency scoring systems to predict in-hospital mortality in the emergency department. BMC Emerg Med 2021;21(1):68. DOI: 10.1186/s12873-021-00459-7.
  5. Rahmatinejad Z, Peiravi S, Hoseini B, Rahmatinejad F, Eslami S, Abu-Hanna A, et al. Comparing in-hospital mortality prediction by senior emergency resident's judgment and prognostic models in the Emergency Department. Biomed Res Int 2023;2023:6042762. DOI: 10.1155/2023/6042762.
  6. Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of patients with COVID-19 requiring intensive care: A cross-sectional study based on machine-learning approach from Iran. Indian J Crit Care Med 2022;26(6):688–695. DOI: 10.5005/jp-journals-10071-24226.
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