Optimizing the Peripheral Venous Duplex Ultrasound Protocol to Minimize Repetitive Strain Injuries among Sonographers
Bibliographic record
Abstract
Background and Objective: Peripheral venous duplex ultrasonography is the standard imaging test to assess patients with known or suspected deep vein thrombosis. The conventional protocol for the lower extremity exam may be time-consuming and ergonomically challenging, increasing the risk for repetitive strain injuries (RSIs) among sonographers. Recognizing the high demand for these ultrasounds and the impact of RSIs on sonographers, we aimed to optimize the lower extremity peripheral venous ultrasound protocol at our institution by addressing exam components that increased exam difficulty or length. Methods: To evaluate where the protocol could be appropriately shortened, we performed (1) a review of current literature, (2) a survey of sonographers, and (3) engagement with a group of radiologists. Results: It was determined that eliminating or minimizing requirements for routine augmentations and assessment of respiratory variation would have limited clinical impact while reducing exam duration and mitigating ergonomic risks associated with peripheral venous exams. Conclusion: Evaluating conventional protocols to optimize practice will help reduce burden on staff, improve efficiency, and ultimately support more effective diagnostic care.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".