Elevated D-dimer is associated with severity of COVID-19: A systematic review and meta-analysis
Bibliographic record
Abstract
With the rapid increase of COVID-19 cases, identifying case severity has become a critical issue for hospital admission and intensive care treatment. Given that pre-existing comorbidities play a significant role in the severity, emerging evidence indicates coagulopathy becomes an independent condition that causes respiratory distress in COVID-19. In this metanalysis, relevant literatures reporting D-dimer, a coagulation byproduct, in COVID-19 cases were synthesized and statistically analyzed to test if the D-dimer level can predict case severity and mortality. The analysis found that D-dimer levels were higher in non-survivors/severe than in survivors/non-severe, (MD 0.64, 95% CI 0.52 to 0.75; participants = 5957, I2 = 98%). Subgroup analysis showed MD between non-survivors and survivors was MD 3.48 μg/mL (95% CI 2.69 to 4.27; participants = 1799; studies = 7; I2 = 86%) with Z-score 8.64, p<0.0001. In meta-regression, a significant correlation was observed between increased plasma mean D-dimer level with increased proportion case severity (P=0.046) and mortality (P=0.009). Overall, the study found that the D-dimer level index can be a predictor of risk for case severity and mortality in COVID-19 patients. The test is rapid and inexpensive and can help clinicians prioritize medical care other than deciding therapeutic options for clinical goals.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".