Patient-reported hope, prognostic understanding, quality of life, symptom burden, coping mechanisms, and financial wellbeing in early phase clinical trial participants.
Bibliographic record
Abstract
12109 Background: Early phase clinical trials (EP-CTs) investigate novel treatment options in oncology, with recent advances in personalized therapy leading to improved outcomes and offering hope to patients with cancer. However, little research has sought to understand hope in EP-CT participants, thus we sought to study associations of patient-reported hope with prognostic understanding, quality of life (QOL), symptom burden, coping mechanisms, and financial wellbeing in EP-CT participants. Methods: We prospectively enrolled consecutive adults with cancer participating in EP-CTs at Massachusetts General Hospital from 04/2021-01/2023. Participants completed baseline surveys prior to treatment initiation that assessed hope (Herth Hope Index [HHI], higher scores indicate greater hope), prognostic awareness (Prognosis and Treatment Perceptions Questionnaire [PTPQ], QOL (Functional Assessment of Cancer Therapy-General), symptom burden (physical: Edmonton Symptom Assessment System [ESAS]; psychological: Patient Health Questionaire-4 [PHQ4]), coping mechanisms (Brief COPE), and financial wellbeing (Comprehensive Score for Financial Toxicity [COST]). We used descriptive statistics and regression models to explore associations of hope with patient characteristics as well as patient-reported prognostic awareness, QOL, symptom burden, coping, and financial wellbeing. Results: Of 221 eligible patients, we enrolled 204 (enrollment rate 92.3%, median age = 63.4 years [range 54.8-70.5]; 56.9% female, and 94.0% metastatic cancer). Most common cancer types were gastrointestinal (34.8%), breast (19.6%), and head and neck (10.3%). Patients had a mean HHI score of 27.2 (range 12.0-36.0), with 27.1% reporting their cancer is curable (mean hope scores were higher for those reporting curable vs incurable on the PTPQ: 28.8 vs 26.7, p = 0.016). Higher hope scores were associated with better QOL (B = 0.21, p < 0.001), lower symptom burden (ESAS-physical: B = -0.10, p < 0.001; PHQ4-depression: B = -2.10 p = < 0.001; PHQ4-anxiety: B = -0.99, p < 0.001), more adaptive coping strategies (self-blame [B = -1.34, p < 0.001]; acceptance [B = 1.30, p < 0.001], denial [B = -1.31, p < 0.001], support [B = 1.03, p < 0.001], active [B = 0.97. p < 0.001], disengage [B = -2.69, p < 0.001], religion [B = 0.63, p < 0.001]), and greater financial wellbeing (B = 0.10, p = 0.013). Conclusions: In this prospective cohort study, we found novel associations of higher hope scores with prognostic understanding, better QOL, lower symptom burden, more adaptive coping mechanisms, and greater financial wellbeing. Future studies should measure patient-reported hope and explore its role as a potential moderator of other outcomes in EP-CT participants.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".