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Record W4400272571 · doi:10.1200/go.24.00043

Financial Toxicity in Cancer Supportive Care: An International Survey

2024· article· en· W4400272571 on OpenAlex
Alexandre Chan, Yu Ke, Mary Anne Tanay, Mary Dagsi, Cristiane Decat Bergerot, Niharika Dixit, Lawson Eng, Ana Cardeña-Gutiérrez, Changchuan Jiang, Ana I. Velázquez, Farhad Islami, Enrique Soto‐Pérez‐de‐Celis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJCO Global Oncology · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsBusinessCancerToxicityFinanceMedicineInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The study aims to explore unmet social needs and sources of financial toxicities in patients as noted by health care professionals and researchers in cancer supportive care, shedding light on potential health disparities. METHODS: In this cross-sectional survey, we anonymously surveyed active members of the Multinational Association of Supportive Care in Cancer (MASCC). The survey, structured in three sections, included questions regarding the routine assessment of social needs during patient consultations, sociodemographic aspects, factors influencing financial toxicity (FT), perceived support for managing FT, and available/desirable resources. RESULTS: A total of 218 MASCC members were included, predominantly from high-income countries (HIC, 73.4%), with many age 41-60 years (56.5%) and female (56.9%). Drug/treatment cost and insurance coverage were the main sources for FT among the HIC, whereas participants from low-middle-income countries (LMIC) considered transportation cost, loss of employment because of cancer diagnosis, and unavailability of return-to-work services as the top three sources of FT. Respondents from LMIC (adjusted odds ratio [aOR], 3.01 [95% CI, 1.15 to 7.93]) and physicians (aOR, 2.67 [95% CI, 1.15 to 6.21]) were more likely to routinely assess financial coverages. Socioeconomic status was consistently ranked as one of the top three sources of financial toxicities by participants from LMIC (34%), HIC excluding the United States (38%), those who do not self-identify as racial/ethnic minority (36%), and physicians (40%). CONCLUSION: This global survey of health care professionals and researchers in HIC and LMIC revealed varying approaches to assessing financial coverage and social needs. Socioeconomic status emerged as a consistent concern across countries, affecting financial toxicities. The study highlights the need for tailored approaches and improved resource visibility while emphasizing clinicians' pivotal role in addressing financial aspects of cancer 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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.050
GPT teacher head0.347
Teacher spread0.297 · 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