Course of COVID-19 Based on Admission D-Dimer Levels and Its Influence on Thrombosis and Mortality
Bibliographic record
Abstract
BACKGROUND: Arterial and venous thrombosis is one of the major complications of coronavirus disease 2019 (COVID-19) infection. Studies have not assessed the difference in D-dimer levels between patients who develop thrombosis and those who do not. METHODS: Our study retrospectively assessed D-dimer levels in all virus confirmed hospitalized patients between May to September, 2020. Patients were divided into three groups: group 1 with normal D-dimer of < 0.5 µg/mL, group 2 with elevation up to six folds, and group 3 with more than six-fold elevation. Statistical analysis was done using SPSS software 23.0. RESULTS: Seven hundred twenty patients (group1 (n = 414), group 2 (n = 284) and group 3 (n = 22)) were studied. Eight thrombotic events were observed. Events were two with stroke, two non-ST elevation myocardial infarction and one each of ST elevation myocardial infarction, superior mesenteric artery thrombosis with bowel gangrene, arteriovenous fistula thrombus and unstable angina. No significant difference (P = 0.11) was observed between median D-dimer levels among patients who developed thrombosis (1.34) and those who did not develop thrombosis (0.91). Twenty-nine patients died. The adjusted odds of death among those with a six-fold or higher elevation in D-dimer was 128.4 (95% confidence interval (CI): 14.2 - 446.3, P < 0.001), while adjusted odds of developing clinical thrombosis was 1.96 (95% CI: 0.82 - 18.2, P = 0.18). CONCLUSIONS: Our study observed a 1.1% in-hospital incidence of clinical thrombosis. While, a six-fold elevation in D-dimer was significantly associated with death; the same was not a strong predictor of thrombosis; an observation which implies that dose of anticoagulation should not be based on absolute D-dimer level.
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 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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".