A toolkit for the collection of thrombosis-related data elements in COVID-19 clinical studies
Bibliographic record
Abstract
Thrombosis has emerged as an important complication of coronavirus disease 2019 (COVID-19), particularly among individuals with severe illness. However, the precise incidence of thrombotic events remains uncertain due to differences in study design, patient populations, outcome ascertainment, event definitions, and reporting. In an effort to overcome some of these challenges and promote standardized data collection and reporting in clinical studies, the American Society of Hematology Research Collaborative COVID-19 Non-Malignant Hematology Task Force, in collaboration with the International Society on Thrombosis and Haemostasis COVID-19 Task Force, developed sets of data elements in the following domains: venous thromboembolism, myocardial infarction, stroke/transient ischemic attack, peripheral arterial thrombosis, bleeding, laboratory investigations, and antithrombotic therapy. Data elements in each of these domains were developed with 3 levels of detail to facilitate their incorporation into studies evaluating a range of interventions and outcomes. Previously published data elements were included where possible. The use of standardized variables in a range of clinical studies can enhance the quality of data collection, create efficiency, enhance comparison of results across studies, and facilitate future pooling of data sets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".