Financial toxicity, quality of life, symptom burden, and access to resources in early-phase clinical trial (EP-CT) participants.
Bibliographic record
Abstract
103 Background: EP-CTs are an important therapeutic option for patients, and financial toxicity represents an increasing concern in oncology. Little is known about the financial toxicity of EP-CT participants. We sought to describe baseline financial toxicity in EP-CT participants and assess associations of this factor with patient characteristics, quality of life (QOL), symptom burden, and access to resources. Methods: We prospectively enrolled consecutive adults participating in EP-CTs at Massachusetts General Hospital from 04/2021 – 01/2023. Participants completed surveys at time of treatment initiation that assessed financial toxicity (Comprehensive Score for Financial Toxicity [COST tool], higher scores indicate greater financial wellbeing). We adapted a financial toxicity grading scale proposed by the creators of the COST tool to define levels: low (COST ≥26) and high (COST 0–25). We surveyed QOL (Functional Assessment of Cancer Therapy-General) and symptom burden (psychological: Patient Health Questionaire-4; physical: Edmonton Symptom Assessment System). Patients were asked their concerns regarding access to resources. We explored associations of financial toxicity with patient characteristics, QOL, symptom burden, and access to resources. Results: Of 221 eligible patients, we enrolled 196 (median age = 62.3 years [range 31.8-88.6], 57.1% female). Most common tumors were gastrointestinal (33.2%) and breast (20.4%); most (93.7%) had metastatic disease. Patients had an average COST score of 28.4 (SD 9.0), with 33.7% reporting high financial toxicity. Patients with high financial toxicity were more likely to be <65 years (70.0% vs 47.7%, p=0.003), unemployed (44.6% vs 17.7%, p<0.001), lack a college degree (52.3% vs 25.6%, p<0.001), and have income <$60,000 (60.6% vs 25.4%, p<0.001) compared to those with low financial toxicity. Compared to those with low toxicity, patients with high financial toxicity also had lower QOL scores (69.9 vs 77.3, p<0.001) and increased psychological (1.3 vs 0.9, p=0.02) and physical (19.3 vs 15.9, p=0.091) symptom burden, although increased physical symptoms did not reach statistical significance. Those with high financial toxicity were more likely to report concerns regarding access to several resources (housing [10.6% vs 2.3%, p=0.033], bill paying [31.8% vs 3.8% 3.8%, p<0.001], food [9.0% vs 0.7%, p<0.006], employment [21.2% vs 1.5%, p<0.001]; there was no significant difference in transportation [22.7% vs 16.1%, p=0.3]). Conclusions: We found that one third of EP-CT participants report high financial toxicity and identified factors associated with higher toxicity. We demonstrated novel associations between high financial toxicity and lower QOL, greater depression, and increased concerns regarding access to resources. Findings highlight the need to address financial toxicity among this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".