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Machine Learning to Predict the Likelihood of Acute Myocardial Infarction

Circulation (New York, N.Y.), 2019-09, Vol.140 (11), p.899-909

2019

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Assessment of the European Society of Cardiology 0-Hour/1-Hour Algorithm to Rule-Out and Rule-In Acute Myocardial Infarction

Circulation (New York, N.Y.), 2016-11, Vol.134 (20), p.1532-1541

2016

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Validation of presentation and 3 h high-sensitivity troponin to rule-in and rule-out acute myocardial infarction

Heart (British Cardiac Society), 2016-08, Vol.102 (16), p.1270-1278

2016

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Acute Kidney Injury and mortality prognosis in Acute Coronary Syndrome patients: A meta‐analysis

Nephrology (Carlton, Vic.), 2018-03, Vol.23 (3), p.237-246

2018

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Diagnostic and prognostic performance of the ratio between high-sensitivity cardiac troponin I and troponin T in patients with chest pain

PloS one, 2022-11, Vol.17 (11), p.e0276645-e0276645

2022

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The utility of presentation and 4-hour high sensitivity troponin I to rule-out acute myocardial infarction in the emergency department

Clinical biochemistry, 2015-12, Vol.48 (18), p.1219-1224

2015

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Association of High-Sensitivity Cardiac Troponin I Concentration With Cardiac Outcomes in Patients With Suspected Acute Coronary Syndrome

JAMA : the journal of the American Medical Association, 2017-11, Vol.318 (19), p.1913

2017

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Determination of Clinically Acceptable Analytical Variation of Cardiac Troponin at Decision Thresholds

Clinical chemistry (Baltimore, Md.), 2024-07, Vol.70 (7), p.967-977

2024

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Performance of the European Society of Cardiology 0/1-Hour, 0/2-Hour, and 0/3-Hour Algorithms for Rapid Triage of Acute Myocardial Infarction : An International Collaborative Meta-analysis

Annals of internal medicine, 2022-01, Vol.175 (1), p.101-113

2022

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The performance of high sensitivity troponin for the diagnosis of acute myocardial infarction is underestimated

Clinical chemistry and laboratory medicine, 2012-04, Vol.50 (4), p.727-729

2012

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Early kinetic profiles of troponin I and T measured by high-sensitivity assays in patients with myocardial infarction

Clinica chimica acta, 2020-06, Vol.505, p.15-25

2020

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Cost and outcomes of assessing patients with chest pain in an Australian emergency department

Medical journal of Australia, 2015-05, Vol.202 (8), p.427-432

2015

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Early Dynamic Change in High-Sensitivity Cardiac Troponin T in the Investigation of Acute Myocardial Infarction

Clinical chemistry (Baltimore, Md.), 2011-08, Vol.57 (8), p.1154-1160

2011

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Next-Day Troponin Tests in Real-World Implementation of Baseline Troponin Rule-Out of Myocardial Infarction Demonstrates Minimal Delayed Troponin Rises

Circulation (New York, N.Y.), 2021-01, Vol.143 (2), p.202-204

2021

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The small number problem in diagnostic algorithms and why we need to bootstrap

Clinical biochemistry, 2017-06, Vol.50 (9), p.540-541

2017

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Detectable High-Sensitivity Cardiac Troponin within the Population Reference Interval Conveys High 5-Year Cardiovascular Risk: An Observational Study

Clinical chemistry (Baltimore, Md.), 2018-07, Vol.64 (7), p.1044-1053

2018

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Systematic Review and Meta‐analysis of Pregnant Patients Investigated for Suspected Pulmonary Embolism in the Emergency Department

Academic emergency medicine, 2014-09, Vol.21 (9), p.949-959

2014

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Making the most of what we have: What does the future hold for Emergency Department data?

Emergency medicine Australasia, 2024-07, Vol.36 (5), p.795-798

2024

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Response by Than et al to Letter Regarding Article, “Assessment of the European Society of Cardiology 0-Hour/1-Hour Algorithm to Rule-Out and Rule-In Acute Myocardial Infarction”

Circulation (New York, N.Y.), 2017-04, Vol.135 (16), p.e923-e924

2017

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Validity of a Novel Point-of-Care Troponin Assay for Single-Test Rule-Out of Acute Myocardial Infarction

JAMA cardiology, 2018-11, Vol.3 (11), p.1108

2018

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The clinical utility window for acute kidney injury biomarkers in the critically ill

Critical care (London, England), 2014-11, Vol.18 (6), p.601-601, Article 601

2014

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Machine learning with D-dimer in the risk stratification for pulmonary embolism: a derivation and internal validation study

European heart journal. Acute cardiovascular care, 2022-01, Vol.11 (1), p.13-19

2022

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Clinical decision support using machine learning and cardiac troponin for the diagnosis of myocardial infarction

European heart journal. Acute cardiovascular care, 2024-08, Vol.13 (8), p.634-636

2024

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Machine Learning to Predict the Likelihood of Acute Myocardial Infarction

Assessment of the European Society of Cardiology 0-Hour/1-Hour Algorithm to Rule-Out and Rule-In Acute Myocardial Infarction

Validation of presentation and 3 h high-sensitivity troponin to rule-in and rule-out acute myocardial infarction

Acute Kidney Injury and mortality prognosis in Acute Coronary Syndrome patients: A meta‐analysis

Diagnostic and prognostic performance of the ratio between high-sensitivity cardiac troponin I and troponin T in patients with chest pain

The utility of presentation and 4-hour high sensitivity troponin I to rule-out acute myocardial infarction in the emergency department

Rapid Rule-out of Acute Myocardial Infarction With a Single High-Sensitivity Cardiac Troponin T Measurement Below the Limit of Detection: A Collaborative Meta-analysis

Association of High-Sensitivity Cardiac Troponin I Concentration With Cardiac Outcomes in Patients With Suspected Acute Coronary Syndrome

Determination of Clinically Acceptable Analytical Variation of Cardiac Troponin at Decision Thresholds

Performance of the European Society of Cardiology 0/1-Hour, 0/2-Hour, and 0/3-Hour Algorithms for Rapid Triage of Acute Myocardial Infarction : An International Collaborative Meta-analysis

The performance of high sensitivity troponin for the diagnosis of acute myocardial infarction is underestimated

Early kinetic profiles of troponin I and T measured by high-sensitivity assays in patients with myocardial infarction

Cost and outcomes of assessing patients with chest pain in an Australian emergency department

Early Dynamic Change in High-Sensitivity Cardiac Troponin T in the Investigation of Acute Myocardial Infarction

Next-Day Troponin Tests in Real-World Implementation of Baseline Troponin Rule-Out of Myocardial Infarction Demonstrates Minimal Delayed Troponin Rises

Effectiveness of EDACS Versus ADAPT Accelerated Diagnostic Pathways for Chest Pain: A Pragmatic Randomized Controlled Trial Embedded Within Practice

The small number problem in diagnostic algorithms and why we need to bootstrap

Detectable High-Sensitivity Cardiac Troponin within the Population Reference Interval Conveys High 5-Year Cardiovascular Risk: An Observational Study

Systematic Review and Meta‐analysis of Pregnant Patients Investigated for Suspected Pulmonary Embolism in the Emergency Department

Making the most of what we have: What does the future hold for Emergency Department data?

Response by Than et al to Letter Regarding Article, “Assessment of the European Society of Cardiology 0-Hour/1-Hour Algorithm to Rule-Out and Rule-In Acute Myocardial Infarction”

Validity of a Novel Point-of-Care Troponin Assay for Single-Test Rule-Out of Acute Myocardial Infarction

The clinical utility window for acute kidney injury biomarkers in the critically ill

Machine learning with D-dimer in the risk stratification for pulmonary embolism: a derivation and internal validation study

Clinical decision support using machine learning and cardiac troponin for the diagnosis of myocardial infarction

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