Survivorship Bias after Military Thoracic Injuries

2011 ◽  
Vol 35 (12) ◽  
pp. 2826-2827
Author(s):  
Calvin S. H. Ng ◽  
Anthony M. H. Ho ◽  
Malcolm J. Underwood ◽  
Tim R. Graham
2011 ◽  
Vol 35 (12) ◽  
pp. 2828-2828
Author(s):  
J. J. Morrison ◽  
M. J. Midwinter ◽  
J. O. Jansen

2007 ◽  
Vol 79 (10) ◽  
Author(s):  
Ryszard Pogorzelski ◽  
Małgorzata Szostek ◽  
Tomasz Wołoszko ◽  
Wawrzyniec Jakuczun ◽  
Sadegh Toutounchi ◽  
...  

2013 ◽  
Vol 48 (14) ◽  
pp. 1097-1101 ◽  
Author(s):  
Daichi Hayashi ◽  
Frank W Roemer ◽  
Ryan Kohler ◽  
Ali Guermazi ◽  
Chris Gebers ◽  
...  

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e049292
Author(s):  
Edward Baker ◽  
Ceri Battle ◽  
Abhishek Banjeri ◽  
Edward Carlton ◽  
Christine Dixon ◽  
...  

ObjectiveThis study aimed to examine the long-term outcomes and health-related quality of life in patients with blunt thoracic injuries over 6 months from hospital discharge and develop models to predict long-term patient-reported outcomes.DesignA prospective observational study using longitudinal survey design.SettingThe study recruitment was undertaken at 12 UK hospitals which represented diverse geographical locations and covered urban, suburban and rural areas across England and Wales.Participants337 patients admitted to hospital with blunt thoracic injuries were recruited between June 2018–October 2020.MethodsParticipants completed a bank of two quality of life surveys (Short Form-12 (SF-12) and EuroQol 5-Dimensions 5-Levels) and two pain questionnaires (Brief Pain Inventory and painDETECT Questionnaire) at four time points over the first 6 months after discharge from hospital. A total of 211 (63%) participants completed the outcomes data at 6 months after hospital discharge.Outcomes measuresThree outcomes were measured using pre-existing and validated patient-reported outcome measures. Outcomes included: Poor physical function (SF-12 Physical Component Score); chronic pain (Brief Pain Inventory Pain Severity Score); and neuropathic pain (painDETECT Questionnaire).ResultsDespite a trend towards improving physical functional and pain at 6 months, outcomes did not return to participants perceived baseline level of function. At 6 months after hospital discharge, 37% (n=77) of participants reported poor physical function; 36.5% (n=77) reported a chronic pain state; and 22% (n=47) reported pain with a neuropathic component. Predictive models were developed for each outcome highlighting important data collection requirements for predicting long-term outcomes in this population. Model diagnostics including calibration and discrimination statistics suggested good model fit in this development cohort.ConclusionsThis study identified the recovery trajectories for patients with blunt thoracic injuries over the first 6 months after hospital discharge and present prognostic models for three important outcomes which after external validation could be used as clinical risk stratification scores.


Cureus ◽  
2020 ◽  
Author(s):  
Misauq Mazcuri ◽  
Tanveer Ahmad ◽  
Ambreen Abid ◽  
Pratikshya Thapaliya ◽  
Mansab Ali ◽  
...  

2018 ◽  
Author(s):  
So Nakashima ◽  
Yuki Sughiyama ◽  
Tetsuya J. Kobayashi

Phenotypic variability in a population of cells can work as the bet-hedging of the cells under an unpredictably changing environment, the typical example of which is the bacterial persistence. To understand the strategy to control such phenomena, it is indispensable to identify the phenotype of each cell and its inheritance. Although recent advancements in microfluidic technology offer us useful lineage data, they are insufficient to directly identify the phenotypes of the cells. An alternative approach is to infer the phenotype from the lineage data by latent-variable estimation. To this end, however, we must resolve the bias problem in the inference from lineage called survivorship bias. In this work, we clarify how the survivor bias distorts statistical estimations. We then propose a latent-variable estimation algorithm without the survivorship bias from lineage trees based on an expectation-maximization (EM) algorithm, which we call Lineage EM algorithm (LEM). LEM provides a statistical method to identify the traits of the cells applicable to various kinds of lineage data.


Resuscitation ◽  
2016 ◽  
Vol 103 ◽  
pp. 66-70 ◽  
Author(s):  
Lucia Ihnát Rudinská ◽  
Petr Hejna ◽  
Peter Ihnát ◽  
Hana Tomášková ◽  
Margita Smatanová ◽  
...  

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