COVID-19, hemostasis disorders and risk of thrombotic complications
Bibliographic record
Abstract
The spread of a new coronavirus infection worldwide since the end of 2019 has becomes a pandemic. Thrombotic complications are the leading cause of death in this disease. After entering the human body, the virus starts a cascade of reactions leading to the development of a cytokine storm, activation of all parts of the hemostasis and complement systems and other changes that result in disturbances in the circulation system with the development of multiple organ failures. Numerous studies have shown that a predictor of a severe course of COVID-19 is a sharp increase of D-dimer concentration in the blood and rise of some other markers of hemostasis activation. Based on the pathogenesis, the developed schemes for the prevention and treatment of COVID-19 severe complications include low molecular weight heparins (LMWH) which are also recommended for an outpatient COVID-19 patient. The prescription of low molecular weight heparin, the duration of their use and doses should be decided on the basis of a risk assessment of factors for each individual patient in combination with laboratory monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".