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Enregistrement 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 sur 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

Notice bibliographique

RevueThe Oncologist · 2025
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueEconomic and Financial Impacts of Cancer
Établissements canadiensKidney Foundation of CanadaInstitute of Cancer ResearchHealth Care FoundationPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésBiomarkerKidney cancerCancerPrecision medicineHealth careHealth professionalsKidney diseasePersonalized medicine

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,155
Score d'incertitude au seuil0,992

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,075
Tête enseignante GPT0,316
Écart entre enseignants0,241 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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