Patient-reported Experience of Diagnosis, Management, and Burden of Renal Cell Carcinomas: Results from a Global Patient Survey in 43 Countries
Bibliographic record
Abstract
The International Kidney Cancer Coalition (IKCC) is a federation of 46 affiliated patient organisations representing 1.2 million patients worldwide that is committed to reducing the global burden of kidney cancer. A large-scale global survey of patients with renal cell carcinoma (RCC) to capture real-world experiences has never been undertaken. The 35-question survey was designed to identify geographic variations in patient education, experience, awareness, access to care, best practices, quality of life, and unmet psychosocial needs. A total of 1983 responses were recorded from 43 countries in 14 languages. Analysis revealed key findings. (1) At diagnosis, 43% of all respondents had no understanding of their RCC subtype. (2) Shared decision-making remains aspirational: globally, 29% of all patients reported no involvement in their treatment decision, responding "My doctor decided for me". (3) While 96% of respondents reported psychosocial impacts, surprisingly, only 50% disclosed them to their health care team. (4) Lastly, 70% of patients were not asked to participate in a clinical trial, although 90% indicated they would be interested. The survey reflects patient perspectives from diverse clinical scenarios in which different treatment options are available. The data point to actionable deficits in the fields of clinical trials, psychosocial support, and shared decision-making. PATIENT SUMMARY: In this brief report, we highlight the key results from the first large-scale global survey of patients with kidney cancer to capture real-world experiences. This survey reflects patient perspectives from diverse clinical scenarios in which different treatment options are available. We conclude that there is a need for improvement in the fields of clinical trials, psychosocial support, and shared decision-making.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".