Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study
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SciScore for 10.1101/2020.04.22.20075143: (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
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:In regard to the specificity exhibited by our models, we can further notice that even while these values are relatively low compared with other tests (which are more specific but slower and less accessible), this may not be too much of a limitation as there is a significant disparity between the costs of false positives and false negatives and in fact our models favors sensitivity (thus, they avoid false negatives). Further, the high PPV (> …
SciScore for 10.1101/2020.04.22.20075143: (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
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:In regard to the specificity exhibited by our models, we can further notice that even while these values are relatively low compared with other tests (which are more specific but slower and less accessible), this may not be too much of a limitation as there is a significant disparity between the costs of false positives and false negatives and in fact our models favors sensitivity (thus, they avoid false negatives). Further, the high PPV (> 80%) of our models suggest that the large majority of cases identified as positives by our models would likely be COVID-19 positive cases. That said, the study presents two main limitations: the first, and more obvious one, regards the relatively low number of cases considered. This was tackled by performing nested cross-validation in order to control for bias [38], and by employing models that are known to be effective also with moderately sized samples [3, 31, 37]. Nonetheless, further research should be aimed at confirming our findings, by integrating hematochemical data from multiple centers and increasing the number of the cases considered. The second limitation may be less obvious, as it regards the reliability of the ground truth itself. Although this was built by means of the current gold standard for COVID-19 detection, i.e., the rRt-PCR test, a recent study observed that the accuracy of this test may be highly affected by problems like inadequate procedures for collection, handling, transport and storage of the swabs, sample cont...
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 10.1101/2020.04.22.20075143: (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 Code availability The complete code will be made available on the Zenodo platform as soon as the work gets accepted for publication. Zenodosuggested: (ZENODO, SCR_004129)Results from OddPub: Thank you for sharing your data.
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 …
SciScore for 10.1101/2020.04.22.20075143: (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 Code availability The complete code will be made available on the Zenodo platform as soon as the work gets accepted for publication. Zenodosuggested: (ZENODO, SCR_004129)Results from OddPub: Thank you for sharing your data.
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, please follow this link.
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