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  1. 4981
    “…OBJECTIVES: Currently, there are no guidelines to help triage nurses identify high‐risk emergency department chest pain patients. Patient self‐reporting of Emergency Department Assessment of Chest Pain Score (EDACS) could facilitate more reliable triage compared to nursing gestalt, but this novel concept is untested. …”
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    Comparison of self‐reported EDACS versus physician‐reported EDACS for the triage of chest pain patients in the emergency department
  2. 4982
    “…Percutaneous coronary intervention (PCI) improves anginal chest pain in most, but not all, treated patients. …”
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    Percutaneous coronary intervention and 30‐day unplanned readmission with chest pain in the United States (Nationwide Readmissions Database)
  3. 4983
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    Chest wall trauma leading to a metallic foreign body in the right subclavian vein: a case report
  4. 4984
    “…The role of chest radiography to diagnose active tuberculosis in symptomatic patients who have a negative Xpert MTB/RIF (Xpert) test result is unclear. …”
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    The role of chest radiography in the diagnosis of bacteriologically confirmed pulmonary tuberculosis in hospitalised Xpert MTB/RIF-negative patients
  5. 4985
    “…Incomplete chest drainage due to chest tube clogging can lead to retained blood after cardiac surgery. …”
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    Active clearance vs conventional management of chest tubes after cardiac surgery: a randomized controlled study
  6. 4986
    “…The growing emergence of colistin resistance in Escherichia coli (E. coli) represents a serious health threat, particularly to intensive care unit (ICU) patients. (2) Methods: In this work, we investigated the emergence of colistin resistance in 140 nosocomial E. coli isolated from patients with pneumonia and admitted to the chest ICU over 36 months. Virulence and resistance-related genes and E. coli pathotypes in colistin-resistant and colistin-sensitive isolates were determined. (3) Results: Colistin resistance was observed in 21/140 (15%) of the nosocomial E. coli isolates. …”
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    Emergence of Nosocomial Pneumonia Caused by Colistin-Resistant Escherichia coli in Patients Admitted to Chest Intensive Care Unit
  7. 4987
    “…BACKGROUND: Whether the sensitivity of Deep Learning (DL) models to screen chest radiographs (CXR) for CoVID-19 can approximate that of radiologists, so that they can be adopted and used if real-time review of CXRs by radiologists is not possible, has not been explored before. …”
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    Comparing a deep learning model's diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs
  8. 4988
    “…The sensitivity of chest CT in suggesting COVID-19 was 98.6% (146/148patients) based on positive RT-PCR results. …”
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    HRCT chest in COVID-19 patients: An initial experience from a private imaging center in western India
  9. 4989
    “…Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. …”
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    Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19
  10. 4990
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    Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality
  11. 4991
    “…BACKGROUND: Chest physiotherapy (CPT) is a non‐pharmacological therapy to facilitate airway secretion removal. …”
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    Safety of mechanical lung vibrator and high‐frequency chest wall oscillation in patients with cardiac implantable electronic device
  12. 4992
    “…In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. …”
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    Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19
  13. 4993
    “…To explore the clinical application value of chest CT quantitative pulmonary inflammation index (PII) in the evaluation of the course and treatment outcome of COVID-19 pneumonia. …”
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    The role of chest CT quantitative pulmonary inflammatory index in the evaluation of the course and treatment outcome of COVID-19 pneumonia
  14. 4994
    “…Primary chest wall abscess due to hematogenous spread is very rare and has seldom been documented in the literature, with most reported cases attributed to Mycobacterium tuberculosis. …”
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    A Rare Case of Escherichia coli Chest Wall Abscess With Rib Osteomyelitis in a Patient With Crohn’s Disease
  15. 4995
    “…BACKGROUND: Chest CT is prognostic in Covid-19 but there is a lack of consensus on how to report the CT findings. …”
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    Visual scoring of chest CT at hospital admission predicts hospitalization time and intensive care admission in Covid-19
  16. 4996
    “…The ease of its dispersion, coupled with its tremendous capability for ailment and death in infected people, makes it a risk to society. The chest X-ray is conventional but hard to interpret radiographic test for initial diagnosis of coronavirus from other related infections. …”
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    Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images
  17. 4997
    “…BACKGROUND: Current strategies to control pain and anxiety of chest tube removal are not efficacious. The aim of this study is to determine the effects of cold therapy and respiratory relaxation exercise on pain and anxiety of chest tube removal. …”
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    Cold Therapy and Respiratory Relaxation Exercise on Pain and Anxiety Related to Chest Tube Removal: A Clinical Trial
  18. 4998
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    Chest X-Ray for Follow-Up of Hospitalized COVID-19 Patients in Settings with Limited Access to Computed Tomography
  19. 4999
    “…In this work, we evaluate the deep learning-based CAD algorithm (DCAD) for detecting and localizing 3 major thoracic abnormalities visible on chest radiographs (CR) and to compare the performance of physicians with and without the assistance of the algorithm. …”
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    Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case–control study
  20. 5000
    “…The aim of the study was to assess the influence of chest compression frequency on the quality of the parameters of chest compressions performed by lifeguards. …”
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    The effect of chest compression frequency on the quality of resuscitation by lifeguards. A prospective randomized crossover multicenter simulation trial
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