Strategies for safe living among lung transplant recipients: a single‐center survey
Bibliographic record
Abstract
BACKGROUND: Lung transplant (LT) recipients are at high risk for infection owing to lifelong immunosuppression and direct communication of the graft with the environment. Guidelines have been established for safe-living strategies after transplantation. We conducted a survey of LT patients to determine compliance with these strategies. METHODS: Adult LT outpatients completed a survey consisting of questions on a 5-point Likert scale with the following categories: hand washing, gardening, respiratory infections, food and water safety, animal contact, travel, and occupation. RESULTS: A total of 194 LT recipients completed the survey (age 54.4 ± 13.3 years; time post transplant 4.76 ± 3.5 years). Regular hand washing was practiced usually or always by 87.6%. Of those who worked with soil/gardened, 70/99 (70.7%) never wore a mask and 15.7% never wore gloves. Pet ownership was common (52%), but most patients used specific precautions during handling. Over one-third of patients continued employment after transplant but, of these, 56% had modified their occupation often because of perceived infectious risks. Most patients were fully compliant with influenza vaccination (92.3%). Patients <40 years of age were less likely to wear long-sleeved clothing in mosquito season (P = 0.002), more likely to handle pet feces (P = 0.005), and less likely to wear a mask with sick contacts (P = 0.021). CONCLUSIONS: We provide important insight into safe-living practices following lung transplantation and identify specific areas and subgroups of patients that could be targeted for enhanced education, with potential significant clinical benefit.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".