Hemostatic laboratory derangements in COVID-19 with a focus on platelet count
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease in 2019 (COVID-19) which rapidly evolved from an outbreak in Wuhan, China into a pandemic that has resulted in over millions of infections and over hundreds of thousands of mortalities worldwide. Various coagulopathies have been reported in association with COVID-19, including disseminated intravascular coagulation (DIC), sepsis-induced coagulopathy (SIC), local microthrombi, venous thromboembolism (VTE), arterial thrombotic complications, and thrombo-inflammation. There is a plethora of publications and conflicting data on hematological and hemostatic derangements in COVID-19 with some data suggesting the link to disease progress, severity and/or mortality. There is also growing evidence of potentially useful clinical biomarkers to predict COVID-19 progression and disease outcomes. Of those, a link between thrombocytopenia and COVID-19 severity or mortality was suggested. In this opinion report, we examine the published evidence of hematological and hemostatic laboratory derangements in COVID-19 and the interrelated SARS-CoV-2 induced inflammation, with a focussed discussion on platelet count alterations. We explore whether thrombocytopenia could be a potential disease biomarker and we provide recommendations for future studies in this regard.
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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 | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".