2022 Global patient survey: Reported experience of diagnosis, management, and burden of renal cell carcinomas.
Bibliographic record
Abstract
653 Background: Kidney cancer (renal cell carcinoma, RCC) has shown a sustained increase in its global prevalence thereby presenting increasing burden to health systems, and most of all, to individual patients and their families. Little is known about the variations in the patient experience and best practices among countries. Although individual national surveys have been held, no conclusions could be drawn about country-level variation in patient experience or best practice. Here, we report on the 3rd biennial Global Patient Survey on the diagnosis, management, and burden of Renal Cell Carcinomas conducted by the International Kidney Coalition (IKCC) and involving its Affiliate Organisations worldwide in 15 languages. The aim of the survey was to improve collective understanding and to contribute toward the reduction of the burden of kidney cancer around the world. Methods: A 35-question survey on the diagnosis, management, and burden of RCC was designed by a multi-country steering committee of patient leaders to identify geographic variations in 6 key dimensions: patient education, experience and awareness, access to care and clinical trials, best practices, quality of life, and unmet psychosocial needs. EAU, ESMO, ASCO and NCCN Guidelines committees provided topics of interest to support evidence-based medicine (eg patient perspective on active surveillance, biopsies, etc) . The survey was distributed to patients with kidney cancer and their caregivers in 15 languages, through social media and IKCC’s 49 Affiliate Organizations and/or allied organizations who are not formal affiliates. It was completed online or in paper form between 26 September 2022 and 31 October 2022. At the time of this abstract submission, the survey was still open for completion. Results: We will present the top-line results of the 2022 survey for the very first time. Survey results will be analyzed using cross-tabulations by an independent third-party organization, and multi-variate analysis of predetermined variable will be performed. The full global report will be presented, as well as individual country reports where at least 100 responses were received. Conclusions: The IKCC and its global affiliates will be using the results to ensure that patients’ voices are heard. Actionable points will suggest future projects. Furthermore, individual countries can use their reports to advance their understanding of patient experiences and to drive improvements in care provision locally.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".