Factors associated with COVID-19-related death using OpenSAFELY
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SciScore for 10.1101/2020.05.06.20092999: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Software and Reproducibility: Data management was performed using Python 3.8 and SQL, with analysis carried out using Stata 16.1 / Python. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and weaknesses: The greatest strengths of this study were speed and size. By building a secure analytics platform across routinely collected live clinical data stored in situ we have been able to produce timely results from the …
SciScore for 10.1101/2020.05.06.20092999: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Software and Reproducibility: Data management was performed using Python 3.8 and SQL, with analysis carried out using Stata 16.1 / Python. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and weaknesses: The greatest strengths of this study were speed and size. By building a secure analytics platform across routinely collected live clinical data stored in situ we have been able to produce timely results from the current records of approximately 40% of the English population in response to a global health emergency. This scale allowed us to work with more precision, on rarer exposures, on multiple risk factors, and to detect important signals as early as possible in the course of the pandemic. The scale of our platform will shortly expand further, and we will report updated analyses over time. Another key strength is our use of open methods: we pre-specified our analysis plan and have shared our full analytic code and all code lists for review and re-use. We ascertained patients’ demographics, medications and comorbidities from their full pseudonymised longitudinal primary care records, providing substantially more detailed information than is available in hospital records or data recorded at time of admission alone, and on the total population at risk rather than the selected subset presenting for treatment in hospital. Linkage to ONS allowed censoring of data in the control population for patients who had died outside hospital or from other causes. Analyses were stratified by area to account for known geographical differences in incidence of COVID-19. We also identify important limitations. Using CPNS data alone relies on hospitals completing a new ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.05.06.20092999: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement This study was approved by the Health Research Authority ( REC reference 20/LO/0651 ) and by the LSHTM Ethics Board ( reference 21863) . Randomization Due to computational time , this was estimated by randomly sampling 5000 patients without the outcome and calculating the C-statistic using the random sample and all patients who experienced the outcome , repeating this 10 times and taking the average Cstatistic . Blinding not detected. Power Analysis The statistical power offered by our approach means that associations with less common risk factors can be robustly assessed in more detail , at the earliest possible date , as the pandemic progresses . Sex as a … SciScore for 10.1101/2020.05.06.20092999: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement This study was approved by the Health Research Authority ( REC reference 20/LO/0651 ) and by the LSHTM Ethics Board ( reference 21863) . Randomization Due to computational time , this was estimated by randomly sampling 5000 patients without the outcome and calculating the C-statistic using the random sample and all patients who experienced the outcome , repeating this 10 times and taking the average Cstatistic . Blinding not detected. Power Analysis The statistical power offered by our approach means that associations with less common risk factors can be robustly assessed in more detail , at the earliest possible date , as the pandemic progresses . Sex as a biological variable 17,18 Study Population and Observation Period Our study population consisted of all adults (males and females 18 years and above) currently registered as active patients in a TPP general practice in England on 1st February 2020. Table 2: Resources
Software and Algorithms Sentences Resources Software and Reproducibility Data management was performed using Python 3.8 and SQL , with analysis carried out using Stata 16.1 / Python . Pythonsuggested: (IPython, SCR_001658)Results from OddPub: Thank you for sharing your code.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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