Adapting the<scp>D</scp>-dimer cutoff for thrombosis detection in elderly outpatients
Bibliographic record
Abstract
D-dimer measurement is an important step in diagnostic strategies for venous thromboembolism. It allows the safe ruling out of the diagnosis with no need for imaging tests in approximately 30% of outpatients. However, the usefulness of d-dimer is limited in elderly patients; the likelihood of a negative d-dimer strongly decreases with age, making physicians reluctant to order the test. Several attempts to improve the performance of D-dimer in elderly patients have been pursued. Recently, an age-adjusted cutoff was derived; the optimal cutoff value (in µg/l) appears to be equal to the patient's age (in years) multiplied by ten in patients over 50 years of age with a low pretest clinical risk of venous thromboembolism. This age-adjusted cutoff value has been extensively and externally validated in retrospective studies that included mostly outpatients with suspected deep vein thrombosis or pulmonary embolism and used various quantitative D-dimer assays. All available studies confirmed the increased usefulness and similar safety of the age-adjusted cutoff compared with the conventional cutoff, the most important benefit being obtained in elderly patients. However, before any recommendation for clinical practice can be made, a prospective diagnostic management outcome study is lacking, in which all low clinical risk patients with D-dimer levels below their age-adjusted cutoff would be left untreated with no further diagnostic testing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".