Travel Practices and Associated Risks in Adult Thoracic Transplant Recipients: A Monocentric Survey
Bibliographic record
Abstract
BACKGROUND: Little is known regarding the travel practices of thoracic organ transplant recipients and their potential associated morbidity. METHODS: A questionnaire was distributed to thoracic organ transplant recipients to capture demographics, risk perception, knowledge regarding vaccination, history of travel outside metropolitan France, pre-travel advice, health issues during travel outside Europe, and travel intentions in the following year. Comparisons were performed between travelers and non-travelers through univariable then multivariable logistic regression. RESULTS: 134 patients completed the survey (72% lung, 11% heart, and 17% heart-lung transplant recipients). Twenty-four percent considered themselves at moderately to significantly increased risk of travel-related health issues. Sixty-two patients (47%) had traveled outside metropolitan France. Among 29 subjects who had traveled outside Europe, 22 had received pre-travel advice. Among 62 respondents who had traveled outside metropolitan France, 6 (10%) experienced health issues (all outside Europe), which led to consultation in three cases and hospitalization in one case. Among 117 respondents, 68 (58%) intended to travel within the following year, and 57 (84%) to seek medical advice before departure, predominantly from their transplant physician. In multivariable analysis, being a lung transplant recipient and higher education level were associated with travel outside Europe. The time post-transplantation was longer for all types of travel, when compared to non-travelers. CONCLUSIONS: Almost half of adult thoracic transplant recipients had traveled outside metropolitan France, 22% outside Europe, and 10% of travelers experienced health issues. The suboptimal preparation of these patients underlines the potential benefits of closer interaction between travel medicine specialists and transplant physicians.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".