COVID‐19 (SARS‐CoV‐2) infection and thrombotic conditions: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: COVID-19 is an infectious disease caused by SARS-CoV-2 associated with haematological manifestations (thrombolytic events). AIMS: Considering the high prevalence of the thrombotic scenarios associated with COVID-19, the aim of this study was to perform a systematic review of the available literature, concerning the relation of COVID-19 and the thrombotic events, and identify prognostic factors for these events. MATERIALS & METHODS: PubMed, Web of Science and Scopus databases were searched. Independent reviewers conducted all flow diagram steps. For qualitative analysis, Oxford level of evidence and Newcastle-Ottawa scale were used in the eligible articles. For the prognostic factors, a meta-analysis was conducted to age, number of neutrophils and platelets, and levels of ferritin, C-reactive protein, lactate dehydrogenase and D-dimer. Publication bias was accessed by funnel plot and by trim-and-fill test. Trim-and-fill test was also applied to evaluate meta-analysis bias. RESULTS: Twenty articles were included in the qualitative analysis, and 6 articles were included in the meta-analysis. Case-control studies showed bias related to exposure, and the main bias in cohort studies were related to selection and outcome. All articles received score 4 for the level of evidence. Hypertension and diabetes were the comorbidities more frequently associated with thrombolytic events. Significant results were found regarding D-dimer (P < .0001) and age (P = .0202) for thrombotic events in patients diagnosed with COVID-19. CONCLUSION: Patients older than 60 years, with hypertension, diabetes and D-Dimer values above 3.17 µg/mL, can be considered prognostic factors for developing thrombotic events due to COVID-19.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".