Impact of cancer types on COVID-19 infection and mortality risk: a protocol for systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has created a huge social and economic burden, and the lifestyles of individuals have significantly changed. In addition, the diagnosis, treatment and management of patients with cancer were greatly affected. Studies have shown that patients with cancer are at a higher risk of COVID-19 infection-related complications, which require aggressive preventive measures. Different types of cancer may have different risks of COVID-19 infection and death, and different preventive care measures are needed for different types of patients with cancer. Here, we designed a protocol for systematic review and meta-analysis to explore the impact of cancer types on COVID-19 infection and mortality risk. METHODS AND ANALYSIS: A systematic search plan will be performed to filter the eligible studies in the seven databases, namely PubMed, Cochrane search strategy, EMBASE search strategy, SinoMed, China National Knowledge Infrastructure, China Science and Technology Journals Database, and Wanfang database from 2019 to 10 August 2021. Two independent reviewers will choose the eligible studies and extract the data. The risk of bias will be evaluated based on the Newcastle-Ottawa Scale recommended by the Cochrane Collaboration. Finally, a systematic review and meta-analysis will be performed using Review Manager (V.5.3) statistical software. ETHICS AND DISSEMINATION: Formal ethical approval is not required, and the findings will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42021271108.
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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 | 0.085 | 0.113 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.006 |
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".