A Global Survey of Self-Reported Cancer Screening Practices by Health Professionals for Kidney Transplant Candidates and Recipients
Bibliographic record
Abstract
Cancer is a major cause of morbidity and mortality in kidney transplant recipients. Health professionals have a critical role in promoting cancer screening participation. From March 2023 to February 2024, an online survey was distributed to kidney transplant health professionals globally to assess their screening practices. We compared their reported screening practices to recommended guidelines and analyzed factors associated with these practices. We received 97 responses, and most were nephrologists (70%), and around 80% recommended breast, colorectal, and cervical cancer screening for kidney transplant candidates and recipients. About 85% recommended lung cancer screening for higher-risk individuals. Skin cancer screening recommendations varied from 69% for transplant candidates and 84% for recipients. Self-reported cervical cancer screening practices were most concordant with recommended guidelines, followed by breast and skin cancers. Barriers reported included a lack of cancer screening awareness (28%), perceived financial constraints (35%), and deficient structured cancer screening systems (51%). Professionals from high-income countries were more likely to advise screening than those from lower-middle-income countries, with odds ratios ranging from 2.9 to 12.3. Most health professionals reported recommending cancer screening for kidney transplant candidates and recipients. However, recommendations were influenced by costs and service delivery gaps within health systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".