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Patient-reported experience of diagnosis, management, and burden of renal cell carcinomas: Results from the 2020 Global Patient Survey from 41 countries.

2021· article· en· W3168280453 on OpenAlexaff
Rachel H. Giles, Deb Maskens, Robin Martinez, Karin Kastrati, Carlos Castro, Juan Carlos Julián Mauro, Robert Bick, Malcolm Packer, Daniel Yick Chin Heng, James Larkin, Axel Bex, Eric Jonasch, Sara MacLennan, Michael A.S. Jewett

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of CalgaryKidney Foundation of Canada
Fundersnot available
KeywordsMedicinePsychosocialFamily medicineQuality of life (healthcare)CancerRenal cell carcinomaKidney cancerNursingPathologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

4579 Background: The sustained increased global prevalence of kidney cancer (renal cell carcinoma, RCC) has increased the burden to health systems, and most of all, to individual patients and their families. 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 second biennial Global Patient Survey on the diagnosis, management, and burden of RCC. Conducted by the International Kidney Cancer Coalition (IKCC) and involving its Affiliate Organizations worldwide, the survey aims 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. The survey was distributed in 13 languages to patients with kidney cancer and their caregivers, through IKCC’s 46 Affiliate Organisations and social media. It was completed online or in paper form between 29 Oct 2020 and 5 Jan 2021. Results: 2,012 (1,586 patients, 417 carers, 9 undisclosed) responses were recorded from 41 countries in 13 languages. Survey results were analyzed using cross-tabulations by an independent third-party organization. The full global report will be publicly available, as well as 7 individual country reports where at least 100 responses were received. 52% lacked understanding of subtype at diagnosis. 42% reported that the likelihood of surviving their cancer beyond 5 years was not explained. 51% reported that they were involved as much as they wanted to be in developing their treatment plan. 41% indicated that “No one” discussed cancer clinical trials with them. 31% were invited to take part in a clinical trial. 56% experienced barriers to their treatment. 45% self-reported that they were insufficiently physically active; 15% were completely sedentary. 50% indicated that they ‘very often’ or ‘always’ experienced disease-related anxiety. 55% indicated that they ‘very often’ or ‘always’ experienced a fear of recurrence. 52% reported having talked to their doctor/healthcare professional about their concerns. Conclusions: The IKCC and its global affiliates will use these results to ensure that patient and caregiver voices are heard and acted upon, with ultimate incorporation of these findings by much broader communities into care pathways, clinical practice, or health technology assessments. Furthermore, individual countries can use their reports to advance understanding of patient experiences and to drive improvements in providing care locally.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.087
GPT teacher head0.340
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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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Citations1
Published2021
Admission routes1
Has abstractyes

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