Clinical features of COVID-19 mortality: development and validation of a clinical prediction model
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SciScore for 10.1101/2020.05.19.20103036: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Data from COVID-19 patients through April 6, 2020 were randomly split into two groups of independent subjects comprising 80% of the sample (n=3841) for development of the mortality predictor (i.e. development set), and 20% (n=961) to serve as retrospective test set 1. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences …SciScore for 10.1101/2020.05.19.20103036: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Data from COVID-19 patients through April 6, 2020 were randomly split into two groups of independent subjects comprising 80% of the sample (n=3841) for development of the mortality predictor (i.e. development set), and 20% (n=961) to serve as retrospective test set 1. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations of the study: Although our datasets likely are the largest that have been used to predict COVID-19 mortality, the clinical features available to us were limited to those routinely collected during hospital encounters. Although we were able to develop accurate predictors from these limited data using our machine learning framework, it should be possible to develop even better predictors using a richer set of features. A key limitation of clinical indices included in the datasets include the uniformity of Electronic Medical Record (EMR)-derived data. For example, while minimum oxygen saturation during the health encounter was identified as a significant predictor for mortality, limitations inherent in the interpretation of this data must be noted, such as the unavailability of the amount of supplemental oxygen being administered at the time of recording and acquisition-related limitations, such as readings below the threshold of accuracy of the monitoring device (e.g. less than 70%). Nonetheless, we found a clearly lower distribution of minimum oxygen saturations in those patients who died from COVID-19 compared to those who survived, highlighting this clinical feature as central to predicting morality for infected patients.
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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