Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: With the threat of a worldwide pandemic of COVID-19, it is important to identify the prognostic factors for critical conditions among patients with non-critical COVID-19. Prognostic factors and models may assist front-line clinicians in rapid identification of high-risk patients, early management of modifiable factors, appropriate triaging and optimising the use of limited healthcare resources. We aim to systematically assess the clinical, laboratory and imaging predictors as well as prediction models for severe or critical illness and mortality in patients with COVID-19. METHODS AND ANALYSIS: All peer-reviewed and preprint primary articles with a longitudinal design that focused on prognostic factors or models for critical illness and mortality related to COVID-19 will be eligible for inclusion. A systematic search of 11 databases including PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, VIP, Wanfang Data, SinoMed, bioRxiv, Arxiv and MedRxiv will be conducted. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data extraction will be performed using the modified version of the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and quality will be evaluated using the Newcastle-Ottawa Scale and the Quality In Prognosis Studies tool. The association between prognostic factors and outcomes of interest will be synthesised and a meta-analysis will be conducted with three or more studies reporting a particular factor in a consistent manner. ETHICS AND DISSEMINATION: Ethical approval was not required for this systematic review. We will disseminate our findings through publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD 42020178798.
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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 | 0.067 | 0.089 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.030 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 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".