Epidemiology, clinical characteristics and treatment of critically ill patients with COVID-19): a protocol for a living systematic review
Bibliographic record
Abstract
INTRODUCTION: In December 2019, the first cases of COVID-19 associated with SARS-CoV-2 viral infection were described in Wuhan, Hubei Province, China. Since then, it has spread rapidly affecting 188 countries and was declared a pandemic by the WHO on 11 March 2020. Preliminary reports suggest up to 30% of patients require intensive care unit (ICU) admission and case fatality rate estimate is 2.3%-7.2%. The primary reason for ICU admission is hypoxaemic respiratory failure, while factors associated with ICU admission include increased age, presence of comorbidities and cytokine storm. Case series and retrospective trials initially assessed proposed treatments with randomised controlled trials now reporting early outcomes. We conduct a systematic review and meta-analysis to identify epidemiological factors, treatments and complications that predict mortality among critically ill patients with COVID-19. METHODS AND ANALYSIS: Our comprehensive search strategy was developed in consultation with a research librarian. We will search electronic databases: Ovid Medline, Ovid Embase, Ovid Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Wiley Cochrane Library. The search strategy combines concepts from COVID-19, validated COVID-19 search filters and geographical locations of large outbreaks. Citation screening, selection, quality assessment and data abstraction will be performed in duplicate. Clinically homogenous epidemiological characteristics, interventions and complications will be pooled in statistical meta-analysis. Within the framework of a living systematic review, the search and data analysis will be updated every 6 months. ETHICS AND DISSEMINATION: Our systematic review will synthesise literature on risk factors and interventions associated with mortality in critically ill patients with COVID-19. Results will be presented at national and international conferences and submitted for peer-reviewed publication. The pooled analysis can provide guidance to inform clinical guidelines for care of critically ill patients with COVID-19. Iterative updates will be made public through open access. Research ethics approval is not required. PROSPERO REGISTRATION NUMBER: CRD42020176672.
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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 | 0.048 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 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".