A comparison of three rapid D‐dimer methods for the diagnosis of venous thromboembolism
Bibliographic record
Abstract
We compared three rapid D-dimer methods for the diagnosis of venous thromboembolism. Patients presenting to four teaching hospitals with the possible diagnosis of deep vein thrombosis or pulmonary embolism were investigated with a combination of clinical likelihood, D-dimer (SimpliRED) and initial non-invasive testing. Patients were assigned as being positive or negative for deep vein thrombosis or pulmonary embolism based on their three-month outcome and initial test results. The three D-dimer methods compared were: (a) Accuclot D-dimer (b) IL-Test D-dimer (c) SimpliRED D-dimer. Of 993 patients, 141 had objectively confirmed deep vein thrombosis or pulmonary embolism. The sensitivity of SimpliRED, Accuclot and IL-Test were 79, 90 and 87% respectively. All three D-dimer tests gave similar negative predictive values. The SimpliRED D-dimer was found to be less sensitive than the Accuclot or IL-Test. When combined with pre-test probability all three methods are probably acceptable for use in the diagnosis of venous thromboembolism.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".