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Cross-sectional study of patients' (pts) and physicians' needs and the financial toxicity of systemic treatment for metastatic renal cell carcinoma (mRCC) in Japan.

2023· article· en· W4324136813 on OpenAlexfundno aff
Takahiro Osawa, Go Kimura, Yasuhisa Fujii, Yosuke Uchitomi, Kazunori Honda, Ariko Otani, Miki Kondo, Tetsuya Wako, Yoshihide Mitsuda, Daisuke Kawai, Michiko SUGAWARA, Hiromi Kitano, Nobuo Shinohara

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
FundersEisai Canada
KeywordsMedicineRenal cell carcinomaObservational studyInternal medicineQuality of life (healthcare)Cross-sectional studyToxicityClinical endpointCancerDiseaseOncologyFamily medicineClinical trialPathologyNursing

Abstract

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702 Background: There is an increasing awareness of the importance of pt-centricity in cancer treatments. To achieve pt-centricity in mRCC treatment, it is important to clarify the differences of preferences between pts and physicians in terms of efficacy, safety, quality of life, and healthcare economics. However, these data are currently lacking for Japanese pts with RCC. This study aims to identify any differences in preferences for drug treatments between pts with mRCC and physicians in Japan, as well as assess the financial toxicity of mRCC and its influencing factors among pts. Methods: This cross-sectional observational study collected data via a web survey of pts with mRCC and physicians. The primary endpoint was to identify the differences in drug-treatment preferences between pts and physicians. The secondary endpoints included identification of the side effects that were most distressing to the pts and those that were most difficult to communicate to the physicians, and the reasons for this. We also evaluated the financial toxicity using the “COST” tool as an exploratory endpoint. Fisher's exact test was used in the evaluation of differences between pts and physicians. Background factors for financial toxicity were evaluated using univariate and multivariate regression analyses. Pearson’s correlation was used to assess the correlation between COST score and FACT-G score. Results: 83 Pts and 165 physicians were included in the analysis. For pts, “eliminating all evidence of disease” ( P < 0.001) was the most important drug-treatment outcome, while for physicians, it was “longer survival” ( P < 0.001). The item of most concern about drug treatment was “disturbing daily activities due to the side effect” for both pts and physicians; while pts were more concerned than physicians about “financial burden” ( P < 0.01) and “lack of the knowledge for the treatment” ( P < 0.001). Diarrhea, fatigue, and vomiting were the most distressing side effects for pts; 51% of pts had difficulty in telling their physicians about side effects such as fatigue, anxiety, and depression. The median COST score was 19 (range, 3–36) and multivariate analysis showed that age and private insurance were independent factors in financial toxicity. In addition, the COST score was positively correlated with the FACT-G total score ( r = 0.40, P < 0.001). Conclusions: There is a gap between pts with mRCC and physicians in their preferences and concerns about drug treatment. Japanese pts with mRCC suffer from side effects, some of which are not shared with physicians, and experience adverse financial impacts even under the universal health insurance coverage system available in Japan. This study highlights the importance of communicating well with pts in clinical practice to achieve pt-centricity in systemic treatment for mRCC.

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)0.001
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.111
GPT teacher head0.375
Teacher spread0.264 · 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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Citations0
Published2023
Admission routes1
Has abstractyes

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