SciScore for 10.1101/2021.10.15.21265021: (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
<table><tr><th style="min-width:100px;text-align:center; padding-top:4px;" colspan="2">Software and Algorithms</th></tr><tr><td style="min-width:100px;text=align:center">Sentences</td><td style="min-width:100px;text-align:center">Resources</td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">These were implemented using the scikit-learn Python library [33], while XGBoost [34] was implemented using scikit-learn compatible packages in Python.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>scikit-learn</div><div>suggested: (scikit-learn, RRID:SCR_002577)</div></div></td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Feature importance and model interpretability was assessed using Shapley additive explanations (SHAP) from the SHAP package [36] in Python.</td><td style="min-width:100px;border-bottom:1px solid lightgray"><div style="margin-bottom:8px"><div>Python</div><div>suggested: (IPython, RRID:SCR_001658)</div></div></td></tr></table>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:
This study has several limitations. The first limitation is the self-reported nature of the survey, which poses risks of over or underreporting. Methods of fraud detection and error handling must be applied if using the model in real-time. The authority for hospitals to recommend their staff to be included in the study may introduce selection bias in the data collection process. Additionally, recall bias may be introduced in answers to retrospective questions in the survey, such as symptoms within the previous 14 days. Future work for the study includes collecting more data for these models, as well as investigating models for using CLM survey and other data to predict additional outcomes, such as hospitalization and mortality. Recruitment of health workers for the study also will expand to several other provinces within Indonesia. Notwithstanding the limitations, our results demonstrate predictive capability for COVID-19 in health workers using machine learning. Our preliminary models showed high predictive performance, especially when trained and tested on similar population groups. When used in practice, CLM can be tuned by training on local populations that resemble target populations in which it will be used. The models can potentially be used to prioritize RT-PCR testing in regions where diagnostic resources are scarce. Allocating testing using the model predictions may lead to reductions in the challenges health workers in Indonesia are facing due to the pandemic. Our ...
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.
Results from scite Reference Check: We found no unreliable references.
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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 checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.
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