Accuracy of D-Dimers to Rule Out Venous Thromboembolism Events across Age Categories
Bibliographic record
Abstract
Background. Strategies combining pretest clinical assessment and D-dimers measurement efficiently and safely rule out venous thromboembolism events (VTE) in low- and intermediate-risk patients. Objectives. As process of ageing is associated with altered concentrations of coagulation markers including an increase in D-dimers levels, we investigated whether D-dimers could reliably rule out VTE across age categories. Method. We prospectively assessed the test performance in 1,004 patients visiting the emergency department during the 6-month period with low or intermediate risk of VTE who also received additional diagnostic procedures. Results. 67 patients had VTE with D-dimers levels above the threshold, and 3 patients displayed D-dimers levels below the threshold. We observed that specificity of D-dimers test decreased in an age-dependent manner. However, sensitivity and negative predictive value remained at very high level in each age category including older patients. Conclusion. We conclude that, even though D-dimers level could provide numerous false positive results in elderly patients, its high sensitivity could reliably help physicians to exclude the diagnosis of VTE in every low- and intermediate-risk patient.
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 | 0.003 | 0.016 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".