Bibliographic record
Abstract
A novel human coronavirus (2019-nCoV) has been declared a pandemic by the World Health Organization (WHO). The rapid spread of this virus in only 2 months highlights its significant global impact, accompanied with a rapid synthesis of evidence surrounding 2019-nCoV. This aim of this review is to analyze the papers on 2019-nCoV published in Jan and Feb 2020 to assess the study quality and publication trends. It aims to provide a reference for future 2019-nCoV research and analyze how society deals with the novel pandemic. Searches A comprehensive search of MEDLINE and EMBASE was conducted on April 21, 2020, with the aid of a medical librarian at McMaster University. We limited our search to papers published from January 1 2020 to April 21, 2020. Results were uploaded to Endnote (Clarivate Analytics) and duplicates were removed. Search Strategies: MEDLINE and EMBASE 1 COVID-19.ti,ab. 2 limit 1 to yr=”2020-Current” 3 2019 n-CoV-2.ti,ab. 4 limit 3 to yr=”2020-Current” 5 SARS-CoV-2.ti,ab. 6 limit 5 to yr=”2020-Current” 7 2019 novel coronavirus.ti,ab. 8 limit 7 to yr=”2020-Current” 9 Wuhan coronavirus.ti,ab. 10 limit 9 to yr=”2020-Current” 11 New coronavirus.ti,ab. 12 limit 11 to yr=”2020-Current” 13 coronavirus.ti,ab. 14 limit 31 to yr=”2020-Current” 15 2 or 4 or 6 or 8 or 10 or 12 or 14 Types of study to be included Any studies pertaining to clinical outcome of COVID-19 Condition or domain being studied Given the introduction of SARS-CoV-2, many papers have been published in the subject area. The main aim of this paper is to systematically identify, appraise the methodology, and summarize the quality of the literature regarding this virus. Participants/population Inclusion of any studies published on DOACs that used clinical data on SARS-CoV-2 in 2020 Interventions, exposures Any publications that used patient data to determine clinical outcomes related to SARS-CoV-2 Comparators The methodological quality on SARS-CoV-2 will be compared to other medical fields that also have similar reviews evaluating the state of the literature Main outcomes The primary outcome is the methodological quality of the included publications that will be assessed using the JBI checklist for case series, SANRA, Newcastle-Ottawa scale for non-randomized studies, and AMSTAR-2 Data extraction Data was extracted from each study independently by two authors (S.I and A.S), and discrepancies were resolved through discussion until agreement was reached between reviewers. Information extracted included journal of publication, impact factor of journal, country of journal basis, date of publication, number of times the paper has been cited, type of study, and topic of study. Journal impact factors were determined using the 2018 InCites Journal Citation Report, and the number of citations of each study was obtained using Google Scholar (Google LLC, Mountain View, Cali.). Risk of Bias (Quality Assessment) Two authors will use the JBI checklist for case series, SANRA, Newcastle-Ottawa scale for non-randomized studies, and AMSTAR-2 to assess the quality of case series, narrative reviews, cohort studies and systematic reviews respectively. Strategy for data synthesis A meta-analysis is not intended to be done. Table, graphs, and descriptive statistics will be used to summarize findings and analysis while accompanied with written statements. Analysis of subgroups or subsets We will use inferential statistics to analyze differences in quality ratings amongst journals (and their impact factor). Impact factor will be determined using the Clarivate 2018 report of journal impact factors. Pearson’s correlation test will be conducted with the data, and a p-value of less than 0.05 will be considered statistically significant. Contact details for further information Allen Li, BHSc Lia59@mcmaster.ca Ali Eshaghpour, BHSc Ali.Eshaghpour@Medportal.ca Organizational affiliation of the review McMaster University, Faculty of Health Sciences Michael D. Degroote School of Medicine, McMaster University, Faculty of Health Sciences Review team members and their organizational affiliations Ms. Sarah Yang, McMaster University, Faculty of Health Sciences Mr. Allen Li, McMaster University, Faculty of Health Sciences Mr. Ali Eshaghpour, Michael D. Degroote School of Medicine, McMaster University, Faculty of Health Sciences Ms. Sofia Ivanisevic, McMaster University, Faculty of Health Sciences Mr. Adrian Salopek, McMaster University, Faculty of Health Sciences Dr. John Eikelboom, McMaster University, Department of Medicine, Hamilton, ON, Canada Dr. Mark Crowther, McMaster University, Department of Medicine, Hamilton, ON, Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.578 | 0.242 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".