Cost-effectiveness of diagnostic strategies for venous thromboembolism: a systematic review
Bibliographic record
Abstract
Guideline developers consider cost-effectiveness evidence in decision making to determine value for money. This consideration in the guideline development process can be informed either by formal and dedicated economic evaluations or by systematic reviews of existing studies. To inform the American Society of Hematology guideline on the diagnosis of venous thromboembolism (VTE), we conducted a systematic review focused on the cost-effectiveness of diagnostic strategies for VTE within the guideline scope. We systematically searched Medline (Ovid), Embase (Ovid), National Health Service Economic Evaluation Database, and the Cost-effectiveness Analysis Registry; summarized; and critically appraised the economic evidence on diagnostic strategies for VTE. We identified 49 studies that met our inclusion criteria, with 26 on pulmonary embolism (PE) and 24 on deep vein thrombosis (DVT). For the diagnosis of PE, strategies including d-dimer to exclude PE were cost-effective compared with strategies without d-dimer testing. The cost-effectiveness of computed tomography pulmonary angiogram (CTPA) in relation to ventilation-perfusion (V/Q) scan was inconclusive. CTPA or V/Q scan following ultrasound or d-dimer results could be cost-effective or even cost saving. For DVT, studies supporting strategies with d-dimer and/or ultrasound were cost-effective, supporting the recommendation that for patients at low (unlikely) VTE risk, using d-dimer as the initial test reduces the need for diagnostic imaging. Our systematic review informed the American Society of Hematology guideline recommendations about d-dimer, V/Q scan and CTPA for PE diagnosis, and d-dimer and ultrasound for DVT diagnosis.
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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 | 0.019 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".