Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees
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SciScore for 10.1101/2020.12.23.20248783: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. 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:Strengths and limitations: Many recently developed prognostic models for COVID-19 rely on information that must be collected post-infection or at admission into a hospital [21, 22]. A key strength of our model is that it …
SciScore for 10.1101/2020.12.23.20248783: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. 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:Strengths and limitations: Many recently developed prognostic models for COVID-19 rely on information that must be collected post-infection or at admission into a hospital [21, 22]. A key strength of our model is that it depends only on historical medical records and demographic variables available before infection. These are variables that are routinely collected and readily available in both public and private medical claims databases used across many countries. Furthermore, Ontario has a diverse population that covers a range of population groups and thus will likely have applicability outside of Ontario or could be easily adapted to score other populations. Although future work with an external dataset would be required to validate the model performance in other geographies, we have observed that models developed on these data can usually be repurposed to other jurisdictions[23–25]. An important strength of our study is the use of Gradient Boosted trees, which allow for highly interpretable models to yield novel insights and relationships among predictor variables. Our study has limitations. Although we have a diverse data source that captures all healthcare interactions, we are limited to some data elements that are not collected in routine data holdings. For example risk factors such as diet and physical activity associated with disease immunity [26] and not captured in our data. Furthermore, recent studies have identified genetic [27], transcriptomic [28], and proteomi...
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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