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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".