Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network
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SciScore for 10.1101/2020.12.13.20247254: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: All patients’ records were completely anonymous, and the data collected has received Institutional Review Board/Ethical Review Committee (IRB/ERC) approval from Aga Khan University Hospital ( Randomization not detected. 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:The Deep-Neo-V model has some limitations in …
SciScore for 10.1101/2020.12.13.20247254: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: All patients’ records were completely anonymous, and the data collected has received Institutional Review Board/Ethical Review Committee (IRB/ERC) approval from Aga Khan University Hospital ( Randomization not detected. 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:The Deep-Neo-V model has some limitations in terms of the available dataset, retrospective nature of the dataset and data form a single hospital, analyzed at admission and day-one data, other observational study confounders may exist and are unaccounted for. In the immediate future we actively look to validate these findings in an external dataset. In the longer term we will continue to develop an algorithm built on the Neo-V Framework approach that has the potential to be implemented, initially in future pandemics because of its ability to accurately predict outcomes using smaller datasets.
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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