American Society of Hematology 2018 guidelines for management of venous thromboembolism: diagnosis of venous thromboembolism
Bibliographic record
Abstract
BACKGROUND: Modern diagnostic strategies for venous thromboembolism (VTE) incorporate pretest probability (PTP; prevalence) assessment. The ability of diagnostic tests to correctly identify or exclude VTE is influenced by VTE prevalence and test accuracy characteristics. OBJECTIVE: These evidence-based guidelines are intended to support patients, clinicians, and health care professionals in VTE diagnosis. Diagnostic strategies were evaluated for pulmonary embolism (PE), deep vein thrombosis (DVT) of the lower and upper extremity, and recurrent VTE. METHODS: The American Society of Hematology (ASH) formed a multidisciplinary panel including patient representatives. The McMaster University GRADE Centre completed systematic reviews up to 1 October 2017. The panel prioritized questions and outcomes and used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess evidence and make recommendations. Test accuracy estimates and VTE population prevalence were used to model expected outcomes in diagnostic pathways. Where modeling was not feasible, management and accuracy studies were used to formulate recommendations. RESULTS: Ten recommendations are presented, by PTP for patients with suspected PE and lower extremity DVT, and for recurrent VTE and upper extremity DVT. CONCLUSIONS: For patients at low (unlikely) VTE risk, using D-dimer as the initial test reduces the need for diagnostic imaging. For patients at high (likely) VTE risk, imaging is warranted. For PE diagnosis, ventilation-perfusion scanning and computed tomography pulmonary angiography are the most validated tests, whereas lower or upper extremity DVT diagnosis uses ultrasonography. Research is needed on new diagnostic modalities and to validate clinical decision rules for patients with suspected recurrent VTE.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.016 |
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".