D-dimer testing, with gender-specific cutoff levels, is of value to assess the individual risk of venous thromboembolic recurrence in non-elderly patients of both genders: a post hoc analysis of the DULCIS study
Bibliographic record
Abstract
Male patients, especially the young, are at a higher risk of recurrent venous thromboembolism (RVTE) than females. Recent scientific reports show the use of D-dimer does not help predict RVTE risk in males. In the present report, we reviewed the data obtained in the DULCIS study (main report published in Blood 2014), focusing on D-dimer results recorded in non-elderly patients of both genders included in the study, and their relationship with RVTE events occurring during follow-up. Using specifically designed cutoff values for positive/negative interpretation, serial D-dimer measurements (performed during warfarin treatment and up to 3 months after discontinuation of anticoagulation) in 475 patients (males 57.3%) aged ≤ 65 years were obtained. D-dimer resulted positive in 46.3% and 30.5% of males and females, respectively (p = 0.001). Following management procedure, anticoagulation was stopped in 53.7% of males and 69.5% of females, who had persistently negative D-dimer results. The rate of subsequent recurrent events was 1.7% (95% CI 0.5-4.5%) and 0.4% (95% CI 0-2.5%) patient-years in males and females, respectively, with upper limits of confidence intervals always below the level of risk considered acceptable by international scientific societies for stopping anticoagulation (< 5%). In conclusion, using sensitive quantitative assays with specifically designed cutoff values and serial measurements during and after discontinuation of anticoagulation, D-dimer testing is useful to predict the risk of RVTE and is of help in deciding the duration of anticoagulation in both male and female adult patients aged up to 65 years.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".