Elevated D-dimer is associated with severity of COVID-19: A systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.025 | 0.587 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.004 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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 it