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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.303 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".