Effectiveness of D-dimer test for detecting deep venous thrombosis in pregnant women with threatened premature delivery and abortion
Bibliographic record
Abstract
Abstract Background Deep vein thrombosis (DVT) in pregnant women often developed pulmonary thrombosis, which could lead to death if detection and treatment are delayed. Thus, early detection and treatment of detect DVT in pregnant women should be performed. The aim of our study was to investigate the normal range of D-dimer levels and identify the cutoff value of D-dimer to detect DVT in pregnant women with threatened premature delivery and abortion.Methods All singleton pregnant women hospitalized for longer than 4 days due to threatened premature delivery and abortion were identified. D-dimer levels were measured at least once per week from hospitalization to delivery. We classified pregnant women into DVT or non-DVT group.Results In our study, 335 pregnant women were included. A total of 3722 blood samples were obtained. There were 6 women in the DVT group and 329 in the non-DVT group. There were more women complicated with thrombophilia in the DVT group (p<0.01). The means and standard deviations of D-dimer levels in the non-DVT group were 1.75±0.19 μg/mL before 28 weeks, 2.75±0.1 μg/mL from 28 to 35 weeks, and 3.22±0.21 μg/mL after 35 weeks. When the cutoff value of the D-dimer to detect DVT was set at 4.8 μg/mL, the sensitivity and specificity were 100% and 85.86%, respectively.Conclusion D-dimer levels of pregnant women with threatened premature delivery and abortion gradually increased as gestational age progressed. The appropriate D-dimer cut-off levels might be useful in the detection of DVT.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".