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Record W4414992675 · doi:10.1093/oncolo/oyaf276.067

66Patient perceptions of biomarker testing in kidney cancer (KC) from the international kidney cancer coalition (IKCC) global patient survey (GPS)

2025· article· en· W4414992675 on OpenAlexaff
Eric Jonasch, Michael A.S. Jewett, Laurence Albigès, Stênio de Cássio Zéqui, Axel Bex, Margaret Hickey, Christine Collins, Karin Kastrati, Jyoti Shah, Deborah Maskens

Bibliographic record

VenueThe Oncologist · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsKidney Foundation of CanadaInstitute of Cancer ResearchHealth Care FoundationPrincess Margaret Cancer Centre
Fundersnot available
KeywordsBiomarkerKidney cancerCancerPrecision medicineHealth careHealth professionalsKidney diseasePersonalized medicine

Abstract

fetched live from OpenAlex

Abstract Background Recent advancements in kidney cancer research have focused on developing and validating new biomarkers for earlier diagnosis, improved prognosis, and personalized treatment strategies. Patient reception of biomarkers can be complex, influenced by factors like familiarity with the testing, health literacy, and communication with healthcare providers. Since 2018 through a biennial GPS, IKCC & its network has captured insights on the pt experience with diagnosis, management and the burden of kidney cancer to identify unmet needs & country variances to help guide development of action recommendations. We present here the findings related to patient perceptions on the use of biomarker testing to determine treatment selection in the future. Methods The survey, designed by an IKCC steering committee of patient advocates, medical experts and the Picker Institute, targeted KC patients and carers. It was cognitively tested, translated into 16 languages, and hosted online. Countries with historic response rates greater than 100 were provided an opportunity to ask five additional questions unique to their local needs of the respondents in their country. Data analysis used cross-tabulations. Results 2677 responses (2049 patients, 628 carers) from 46 countries were collected between September 24 and November 15, 2024. Respondents: 54% male; 80% aged 46–80; 62% ccRCC; 19% stage 4 at diagnosis; 52% were diagnosed in the past four years. Globally, when asked how they would feel about their doctor using the results of potential future biomarker tests to guide their treatment choice, 29% would trust biomarker testing, 22% have some reservations and questions but generally trust the process, 25% were concerned about relying only on a biomarker test and (23%, n = 552) did not know. These results vary significantly by country, but variances were noted by age, sex, and stage of disease. In the USA (n = 220) additional questions specific to US respondents were asked probing patient involvement and interest in personalized treatment strategies, circulating tumor cells and genomic testing. Circulating Tumor Cells: 13% were offered the test with 12% being tested, 63% were not offered testing but would like to have had it offered. Genomic Testing: 27% were offered the test and were tested, 53% were not offered testing but would like to have been. Conclusions IKCC GPS is the only worldwide KC survey measuring the experiences of people affected by KC and captured feedback from a record number of respondents. Most patients have some reservations about the use of biomarkers to guide treatment decisions in the future which can be addressed with appropriate education in the shared decision-making process. In the USA, most patients were receptive to circulating tumor cells and/or genomic testing. Patient education addressing the role of biomarkers is essential for clinical research studies and KC care in the future to ensure patients can make informed decisions. Questions about the willingness to pay for potential biomarker tests when available, and the bioethical concerns of positive germline genetic testing should be considered in future surveys.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.075
GPT teacher head0.316
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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