ESMO expert consensus statements on the screening and management of financial toxicity in patients with cancer
Bibliographic record
Abstract
BACKGROUND: Financial toxicity, defined as both the objective financial burden and subjective financial distress from a cancer diagnosis and its treatment, is a topic of interest in the assessment of the quality of life of patients with cancer and their families. Current evidence implicates financial toxicity in psychosocial, economic and other harms, leading to suboptimal cancer outcomes along the entire trajectory of diagnosis, treatment, supportive care, survivorship and palliation. This paper presents the results of a virtual consensus, based on the evidence base to date, on the screening and management of financial toxicity in patients with and beyond cancer organized by the European Society for Medical Oncology (ESMO) in 2022. METHODS: A Delphi panel of 19 experts from 11 countries was convened taking into account multidisciplinarity, diversity in health system contexts and research relevance. The international panel of experts was divided into four working groups (WGs) to address questions relating to distinct thematic areas: patients with cancer at risk of financial toxicity; management of financial toxicity during the initial phase of treatment at the hospital/ambulatory settings; financial toxicity during the continuing phase and at end of life; and financial risk protection for survivors of cancer, and in cancer recurrence. After comprehensively reviewing the literature, statements were developed by the WGs and then presented to the entire panel for further discussion and amendment, and voting. RESULTS AND DISCUSSION: A total of 25 evidence-informed consensus statements were developed, which answer 13 questions on financial toxicity. They cover evidence summaries, practice recommendations/guiding statements and policy recommendations relevant across health systems. These consensus statements aim to provide a more comprehensive understanding of financial toxicity and guide clinicians globally in mitigating its impact, emphasizing the importance of further research, best practices and guidelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.185 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".