Travel medicine, transplant tourism, and the solid organ transplant recipient—Guidelines from the American Society of Transplantation Infectious Diseases Community of Practice
Bibliographic record
Abstract
These updated guidelines from the Infectious Diseases Community of Practice of the American Society of Transplantation review recommendations for prevention and management of travel-related infection in solid organ transplant (SOT) recipients as well as risks associated with transplant tourism. Counseling regarding travel post-transplant should be included during the pre-transplant evaluation, and all SOT recipients should be seen by a travel medicine specialist prior to traveling to destinations with higher rates of infection. Patients should be advised on vaccine-preventable illnesses as well as any need for prophylaxis (ie, malaria) based on their individual travel itineraries. Information with regards to specific recommendations for vaccines and prophylactic medications, along with drug-drug interactions, is summarized. Counseling should be provided for modifiable risks and exposures (ie, food and water safety, and insect bite prevention) as well as non-infectious travel topics. These guidelines also briefly address risks associated with transplant tourism and specific infections to consider if patients seek care for transplants done in foreign countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".