Severity and mortality of COVID-19 among people with disabilities: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: As the COVID-19 pandemic and the subsequent global healthcare crisis continue, people with disabilities may face greater health risks than their non-disabled peers. This systematic review and meta-analysis aims to determine the severity and mortality of COVID-19 among people with different types of disabilities. METHODS AND ANALYSIS: We will search PubMed, HINARI, ScienceDirect, PEDro and Cochrane Library databases. Grey literature search will also be conducted on MedRxiv and Google Scholar. Searches will be without date restrictions. Cohort, case-control and cross-sectional studies assessing the severity and mortality of COVID-19 among people with disabilities will be included. Only full-text studies in the English language will be included. The outcomes of interest include the risk of COVID-19 infection, rate of hospitalisation, severity, hospital stay, mortality and others variables where data are available. Two reviewers will extract data and perform risk of bias assessment independently. The Newcastle-Ottawa Scale will be used to assess risk of bias. Review Manager V.5.4 and Stata V.16.0 software will be used for statistical analysis. Heterogeneity will be analysed using I² statistics. Pooled OR with 95% CI will be used to calculate the pooled results for outcome variables. ETHICS AND DISSEMINATION: Ethical approval and informed consent are not required as this is a systematic review of existing publications. The final results will be published in a peer reviewed journal and presented at national and international conferences. PROSPERO REGISTRATION NUMBER: CRD42022306361.
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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 | 0.080 | 0.105 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.008 |
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".