Destination Therapy: Safety and Feasibility of National and International Travel
Bibliographic record
Abstract
Results for Destination Therapy (DT) continue to improve with advanced technology, better patient selection, and experienced clinical management. Quality of life for these patients is an important component of the overall success of DT, and traveling is becoming more common. We reviewed our experience with long-distance travel in our DT population. All patients implanted with a left ventricular assist device for DT were followed prospectively. Long-distance travel was considered to be >200 miles, one way from their homes. There were 15 patients (14 men) with an average age of 66 years (range, 30-82) who traveled a combined total of 40 long-distance trips. Four trips were international (Spain, Canada (2), and Puerto Rico), 35 within the continental U.S., and one to Hawaii. The average one way distance traveled was 925 miles with a range of 218-4256 miles. The average time away from home was 8.3 days (range, 2-30). Patients traveled by airplane (17), car (23), and one trip included a 5 day cruise. Five complications occurred: driveline trauma, delay of reentry into the United States, missed flight, red heart alarm from bearing wear, and dehydration. All patients returned home safely for routine follow-up. Long-distance travel is possible for DT patients. Anticipating potential problems and careful planning is necessary for safe national and international travel.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".