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Working with categorical outcome variables
Published in Ewen Harrison, Pius Riinu, R for Health Data Science, 2020
In the section below, we convert the continuous variables to (e.g., ), then use the forcats package to recode the factor levels. Modern databases (such as REDCap) can give you an R script to recode your specific dataset. This means you don’t always have to recode your factors from numbers to names manually. But you will always be recoding variables during the exploration and analysis stages too, so it is important to follow what is happening here.
Patient-Reported Outcome Measures in Neurogenic Bladder
Published in Jacques Corcos, Gilles Karsenty, Thomas Kessler, David Ginsberg, Essentials of the Adult Neurogenic Bladder, 2020
The SCI-QOL is a set of QOL questionnaires that assess various aspects of life after a SCI.16 As part of this process, two bladder-specific modules were published in 2015: the SCI-QOL bladder management difficulties and the SCI-QOL bladder complications scale.17 Item-response theory was used to develop computer adaptive test versions of each of these scales, and paper-based complete and short forms versions are also available. The SCI-QOL bladder management difficulties module includes 15 questions covering topics such as worries about bladder management and incontinence, and the impact of bladder management daily activities. The SCI-QOL bladder complications module asks six questions about the impact of urinary infection symptoms and their effect on daily activities. They are available through the National Institutes of Health toolbox, and as an added convenience they are preprogrammed into the REDCap common library.
The Clinical Utility of Computed Tomographic Scanning and Neurologic Examination in the Management of Patients with Minor Head Injuries
Published in Stephen M Cohn, Ara J. Feinstein, 50 Landmark Papers every Trauma Surgeon Should Know, 2019
SR Shackford, SL Wald, SE Ross, TH Cogbill, DB Hoyt, JA Morris, PA Mucha, HL Pachter, HJ Sugerman, K O’Malley, J Trauma
The success and the strength of any multicenter design are dependent upon the degree of commitment of each principal investigators (PI), and the willingness of the PI to be free of confirmation bias when submitting data. Commitment was critical as this work was performed with rudimentary computer data capture and analysis (i.e., prior to the introduction of such tools as REDCap).
Test-Retest repeatability of automated threshold audiometry in Nicaraguan schoolchildren
Published in International Journal of Audiology, 2023
Sarah Y. Bessen, Isabelle L. Magro, Karen Mojica Alvarez, Devin R. Cowan, Donoso Peñalba, Abigail Fellows, Marvin Gonzalez-Quiroz, Catherine Rieke, Jay C. Buckey, Christopher Niemczak, James E. Saunders
Sound attenuation of the headset averages between 30 and 44 dB from 250 to 8000 Hz (Meinke et al. 2017). ATA testing protocols were developed using TabSINT, an application developed by Creare, LLC (Hanover, NH) used to develop and customise tests, such as tablet-based hearing exams, as well as general questionnaires. ATA testing was completed at 1000, 2000, and 4000 Hz as recommended by the American Academy of Audiology for childhood screening (Bright et al. 2011). ATA data captured through the WAHTS/TabSINT system included threshold measurements for each ear (left vs right) and frequency. Demographic data were collected, entered, and managed using tablet-based Research Electronic Data Capture (REDCap) tools hosted through Dartmouth College. REDCap is a secure, web-based software platform designed to support research data collection (Harris et al. 2009). In addition, height, weight, and head circumference were measured during the testing session and entered into REDCap to assess the potential impact of headset fit on testing repeatability. TabSINT audiometric data and REDCap data were synchronised by use of a QR Code identifier to minimise confusion of subjects. Once an internet connection was available (generally by cellular network), all data were subsequently uploaded into a REDCap-based Mobile Hearing Management System for review by a Nicaraguan otolaryngologist (KM).
Evaluation of an assessment scale for aesthetic outcome in breast reconstructions based on digital photos in both 2D and 3D format
Published in Journal of Plastic Surgery and Hand Surgery, 2023
Linda Tallroth, Nathalie Mobargha, Patrik Velander, Stina Klasson, Magnus Becker
The study data were managed and collected using Research Electronic Data capture (REDcap) tools hosted at Lund University [14,15]. REDcap is a web-based platform which we used to facilitate the photo assessments. The study was performed in two phases. In the first phase, 48 sets of photos accompanied by the assessment scale were included. The same breast reconstruction appeared on two sets of photos. There were four photos per set in 2D format and five per set in 3D format (Figure 1(A–D)). The sets were arranged in a randomised order. Laterality was noted but not reconstruction type. To facilitate a high response rate, the assessments could be completed at any time. The panellists were not informed in advance that the same reconstruction appeared twice, nor that two different camera modalities were used. All panellists were asked to perform the assessment twice, a minimum of three weeks apart. They were also asked to record the time it took to perform the assessment. An expert and a layman panel assessed the photos in the study’s first phase and a reliability analysis was conducted. In the second phase of the study, all breast reconstructions apart from two were replaced by new breast reconstructions. Twelve breast reconstructions were included, and in total there were 24 sets of photos. An expert, a layman and a patient panel assessed the breast reconstructions in the second phase. The assessment was performed twice by the patients and once by the other panels.
No difference in subsequent trainee satisfaction associated with in-person exposure prior to remote interviews
Published in Medical Education Online, 2022
Jocelyn Simmers, Nevada Cox, Beth Herman, Joslyn Kirby
A cross-sectional, self-reported survey study was conducted in September to October 2021 and May 2022. The survey was developed, piloted, and data collected and managed using REDCap electronic data capture tools hosted at Penn State University [10]. REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies. An online link to the anonymous survey (Supplement) was sent to new residency and fellowship trainees after approximately 3 and 11 months from their start of training at an academic medical center. An invitation to complete the survey was sent from the staff from the Office of Graduate Medical Education, along with a reminder every five days and a maximum of four invitations. Upon completion of the survey, participants were offered a chance in a drawing to win a $50 gift card. Those who reported participation in-person interviews for their program were excluded.