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Patient-reported experience of diagnosis, management, and burden of renal cell carcinomas: Results >2,000 patients in 41 countries, with focus on older patients.

2022· article· en· W4212983849 on OpenAlexaff
Rachel H. Giles, Lorenzo Marconi, Robin Martinez, Deborah Maskens, Karin Kastrati, Carlos Castro, Juan Carlos Julián Mauro, Robert Bick, Daniel Yick Chin Heng, James Larkin, Axel Bex, Eric Jonasch, Sara MacLennan, Michael A.S. Jewett

Bibliographic record

VenueJournal of Clinical Oncology · 2022
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
KeywordsMedicinePsychosocialQuality of life (healthcare)Family medicineRenal cell carcinomaCancerNursingInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

306 Background: Renal cell carcinoma (RCC) is increasing in global prevalence, thereby increasing burden to health systems, and most of all, to individual patients and their families. Little is known about the variations in patient experience and best practices among countries. Here, we report on the second biennial Global Patient Survey on the diagnosis, management, and burden of Renal Cell Carcinomas conducted by the International Kidney Coalition (IKCC) worldwide in 13 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 to identify geographic variations in 6 topics: patient education, experience and awareness, access to care and clinical trials, best practices, quality of life, and unmet psychosocial needs. The survey was distributed to patients with kidney cancer and their caregivers in 13 languages, through IKCC’s 46 Affiliate Organisations and social media from 29 Oct 2020 to 5 Jan 2021. Results: 2,012 responses came from 41 countries. Survey results were analysed using cross-tabulations by an independent third-party organisation. The full global report is publicly available, as well as 7 individual country reports where at least 100 responses were received. 42% reported that the likelihood of surviving their cancer beyond 5 years was not explained Just over half (51%) reported that they were involved as much as they wanted to be in developing their treatment plan. 56% experienced barriers to their treatment 41% indicated that “No one” discussed cancer clinical trials with them 31% were invited to take part in a clinical trial 45% self-reported that they were insufficiently active 50% indicated that they ‘very often’ or ‘always’ experienced disease-related anxiety. 26% ‘very often’ or ‘always’ experienced stress related to financial issues 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 48% had been offered a biopsy in the past with only 3% refusing; 47% would be willing to undergo biopsy in the future Patients aged ≤65 experienced more barriers to quality care, understood their disease less well, and experienced a longer time to diagnosis. 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. Individual countries can use their reports to advance their understanding of patient experiences and to improve local care.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
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)0.0020.001

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.

Opus teacher head0.044
GPT teacher head0.307
Teacher spread0.263 · 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
Published2022
Admission routes1
Has abstractyes

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