Pattern Analysis of Lower Extremity Venous Thrombosis: Implications for Point of Care Ultrasound (POCUS) Protocols
Bibliographic record
Abstract
Introduction Emergency department point-of-care ultrasound (POCUS) can identify lower extremity venous thrombosis (LEVT) with a published accuracy is 85–90%. The aim of this study was to compare the patterns of LEVT with protocol results and determine the clinical impact of the study results. Methods Patterns of superficial venous thrombosis(SVT) and deep venous thrombosis (DVT) were collated from positive venous duplex ultrasound (VDU) studies. Each pattern was mapped to the potential findings by the described POCUS protocols. Analysis of the literature was used to identify the potential clinical impact of the findings and the functional efficacy of each strategy and a numerical result was developed. Results One hundred six studies were positive for DVT (42), SVT (44), or both (20) on VDU. Patterns for DVT (single or multiple levels and unilateral or bilateral) and SVT (great saphenous vein above and/or below knee or small saphenous vein in single, multiple or bilateral and juxta-junctional) were noted. The patterns covered by the “two-area” protocol showed DVT = 80% and SVT = 38%, and by “three-point compression” DVT = 74% and SVT = 0%. Particular areas not covered included proximal disease (iliac and vena cava) and calf DVT and SVT in all areas except juxta-junctional. The potential impact for DVT is high, whereas for SVT it is moderate to low. The functional efficacy of the “two-area” protocol (5.9) exceeds the “three-point compression” strategy (3.7) but falls short of the “gold standard” VDU (10). Conclusion Pattern analysis of venous thrombosis identifies weakness in POCUS strategies; the clinical implications allow for an assignment of the functional efficacy of each study. Knowledge of these findings should inform emergency room POCUS strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".