Camilla Zimmermann

Background: Decisional regret (DR) is an important patient-reported outcome, reflecting patients’ retrospective evaluation of healthcare decisions. In oncologic settings, treatment choices typically involve trade-offs between survival, toxicity and/or quality of life, which may influence DR after decisions are made. This scoping review aimed to map the existing literature on DR among adult patients with cancer and to characterise how DR has been measured, reported and associated with clinical and psychosocial factors across oncology contexts. Methods: MEDLINE, EMBASE and Cochrane CENTRAL were searched from database inception to the search date. Studies involving adults with cancer that measured DR using a defined instrument were included. Data were extracted on study characteristics, cancer types, treatment context, measurement tools, timing of DR assessment and associated factors. Findings were synthesised descriptively and thematically. Results: Among 858 identified records, 104 studies were included in this review. The Decisional Regret Scale was the most frequently used instrument. Most studies assessed DR after treatment completion, commonly within 24 months following treatment decisions. DR levels were generally low to moderate. Higher DR was associated with treatment-related morbidity, psychological distress, inadequate information, limited involvement in decision-making and discordance between expectations and outcomes. Conclusion: DR in oncology is influenced by clinical outcomes and quality of the decision-making process. Greater emphasis on shared decision-making, communication and expectation management may help reduce DR and improve patient-centred cancer care. Keywords: Decisional regret, cancer, decision regret scale, treatment decision making, psychosocial factors

Objectives: Time toxicity—the time patients spend receiving treatment and associated care—is an increasingly recognised burden of cancer treatment. Literature has largely relied on calendar-day–based measurement, which may obscure meaningful variation in time burden. This study aimed to quantify time toxicity with greater granularity by measuring it in hours. Methods: We conducted a retrospective cohort study of patients with recurrent prostate cancer enrolled in a clinical trial of concurrent high-dose-rate brachytherapy and hormonal therapy at a tertiary cancer centre in Canada. Electronic medical records were reviewed for 180 days following treatment initiation. Time toxicity was quantified as median days and median hours per month spent in care. Subgroup analyses were conducted by encounter type. Sensitivity analyses incorporated the estimated travel time. Type I error was 0.05. Results: 46 patients were included. Patients had a median of 6 healthcare encounters: 3 in Month 1, with the remaining 3 distributed across the following 5 months. Early encounters were predominantly treatment-related, whereas later encounters were largely monitoring or follow-up appointments. The median time toxicity was 13.7 hours over 6 months following treatment initiation, with 10.9 hours occurring in Month 1 and minimal time in subsequent months. Planned, in-person encounters accounted for nearly all toxicity. Incorporating travel time more than doubled time toxicity in sensitivity analyses. Conclusions: Time toxicity was front-loaded and driven primarily by planned, in-person care. Expressing time toxicity in hours provided clearer quantification of early time burden. More granular, patient-centred measurement of time toxicity should be prioritised in future cancer trials.

Purpose Financial toxicity is increasingly recognised as a significant consequence of cancer treatment yet little is known about its impact on patients with cancer enrolled in pharmacological clinical trials. This review aimed to quantify direct medical and indirect non-medical financial toxicities experienced by these patients, and assess how they are measured and reported. Design We conducted a systematic review of Ovid Medline, Ovid Embase and the Cochrane Central Register of Controlled Trials from inception to 30 April 2025. Studies were included if they reported on direct or indirect financial toxicity among patients with cancer enrolled in pharmacological clinical trials. Cost data were extracted and adjusted to 2025 US dollars. Study methodology and outcomes were noted. Study quality was assessed using the AXIS tool. Results Three studies, all conducted in the USA, were included. Two reported direct financial toxicity of US$250–US$300 a month and two reported indirect toxicity exceeding US$800/month. Key cost drivers for indirect toxicity included housing and travel-related expenses. All studies had methodological concerns, including the use of unvalidated self-developed surveys and omission of key cost domains, such as lost income and caregiver burden. Conclusions There is sparse evidence, but this review suggests that financial toxicity among trial participants is substantial, and may exceed that of the general cancer population. Further research using standardised, validated methods across diverse contexts is needed.

Context: Enhancing end-of-life care requires valid measurement of the quality of dying and death, but existing measures of this outcome have limited applicability across varied cultural and resource settings. To address this gap, we revised the Quality of Dying and Death Questionnaire (QODD) to create the QODD-Revised Global Version (QODD-RGV). Objectives: Our objective was to conduct a preliminary evaluation of the content validity of the QODD-RGV questionnaire in North American inpatient hospice settings based on item relevance, comprehensibility, and comprehensiveness. Methods: Bereaved caregivers of patients who died in inpatient hospices in Canada and the United States were recruited 4–6 months after death of the patient. Participants completed the QODD-RGV, a 26-item measure assessing the experience of patients in the last week of life regarding symptoms, care preferences, and social support. Cognitive interviews were conducted with verbal probing and think-aloud techniques to evaluate the participants’ understanding of the items and the information they relied upon to make their ratings. Interview transcripts were coded independently by two team members followed by consensus coding. Qualitative content analysis was performed. Results: Eighteen cognitive interview protocols were completed. Participants indicated that their ratings were most commonly based on information from the perspective of the patient or themselves. The most common judgment strategy was comparison to “a hoped for or ideal dying experience,” followed by “a state of distress/no distress.” All respondents used multiple standards of comparison. Response difficulty was most frequently due to lack of communication with the patient. No single item was consistently misunderstood, but there was uncertainty about the ratings of some items due to limited caregiver knowledge or time spent communicating with the patient. Conclusion: The present study with bereaved caregivers in North American inpatient hospice settings provides preliminary evidence supporting the content validity of the QODD-RGV in North American hospice settings based on relevance, comprehensibility, and comprehensiveness of the items.

Background: Death anxiety is common in advanced cancer, but little is known about risk factors for its occurrence, frequency or severity in those receiving specialized palliative care. Aim: This study aimed to determine the prevalence and correlates of death anxiety in outpatients with advanced cancer receiving specialized palliative care. Design: Cross-sectional observational study. Setting/Participants: We identified the prevalence of death anxiety in 203 patients with advanced cancer attending an outpatient palliative care clinic in a large tertiary care hospital, and examined its association with sociodemographic factors, time since advanced cancer diagnosis, time since initial palliative care consultation, and proximity to death. Death anxiety was measured using the Death and Dying Distress Scale (DADDS). Variables that demonstrated significant univariable associations were subsequently included in a multivariable regression analysis. Results: Moderate to severe death anxiety (DADDS score ⩾ 25) was reported by 46.3% of participants. Multivariable analysis demonstrated that greater death anxiety was associated with lower household income (⩾$100,000+ vs <$30,000: ß = −0.30, p = 0.002; $60,000–$99,999 vs <$30,000: ß = −0.27, p = 0.004; $30,000–$59,999 vs <$30,000: ß = −0.31, p < 0.001) and living alone ( ß = 0.16, p = 0.03), but not with time since advanced cancer diagnosis or initial palliative care consultation, or with proximity to death. Conclusions: Moderate to severe death anxiety is reported by almost half of patients with advanced cancer receiving outpatient palliative care and is associated with economic disadvantage and solitary living arrangements. Further research is needed to determine to what extent targeted psychosocial and palliative interventions can alleviate such distress in this population.

Purpose: Time toxicity—the time patients spend receiving treatment, managing side effects, attending follow-ups and undergoing rehabilitation—is increasingly recognised as an important burden of cancer care. While time toxicity has been studied in routine clinical settings, it has rarely been quantified among patients with cancer enrolled in pharmacotherapy clinical trials. This systematic review aims to quantify time toxicity in this population. Design: We searched Ovid MEDLINE, Embase and Cochrane CENTRAL from inception to 12 June 2025. Studies were included if they reported time toxicity among patients with cancer enrolled in clinical trials. Time toxicity was defined as calendar days with healthcare contact. Primary outcomes were (1) total days, (2) planned days and (3) unplanned days per month. Study quality was assessed using the Joanna Briggs Institute’s (JBI) Checklist for Cohort Studies. Results: Four retrospective studies reporting on an aggregate of 1809 patients were included. Two studies had low risk for bias and two had unclear risk of bias. Patients experienced time toxicity a mean of 7.6 days per month, with the highest burden in the first month of trial participation. Planned and unplanned days averaged 2.2 and 2.0 days per month, respectively. The ratio of planned to unplanned days remained consistent over time. Conclusions: Time toxicity is a substantial, under-recognised burden in cancer pharmacotherapy trials—approximately two times that experienced by non-participants. Its measurement and mitigation should be prioritised to improve patient-centred care, support shared decision-making and inform trial design, policy and consent processes. Registration: Study protocol preregistration: https://doi.org/10.17605/OSF.IO/BVY7C .

Background: This study aimed to examine the number of FDA-approved cancer pharmacotherapies and analyse pivotal study characteristics over time, including sample sizes. Methods: We developed a web scraper to collate a cohort of FDA-approved cancer pharmacotherapies from 1953 until 31 December 2024. For each pharmacotherapy, details of pivotal studies leading to approval were recorded, including protocol and final sample size, study design, therapy type, and quality assessment using Cochrane Risk of Bias tools. We used regression analyses and discretization to identify trends in sample size. Type I error was set at 0.05. (Study protocol pre-registration: https://doi.org/10.17605/OSF.IO/KVA23) Results: We identified 255 pharmacotherapies, supported by 306 pivotal studies; 125 (49%) were targeted pharmacotherapies, 61 (24%) chemotherapies, 47 (18%) immunotherapies, 21 (8%) hormonal therapies, and 1 (0.4%) other. The median sample size was 290 (IQR = 427); sample sizes increased in the 1990s (median = 407) and remained stable thereafter. Stratified analysis demonstrated smaller sample sizes for phase 1 and 2 studies before 1980, with no change in phase 3 studies. For 165 studies reporting protocol sample sizes, studies from 2020-2024 (median = 147.5) were smaller than those from 2010-2019 (median = 320). Conclusions: The increase in sample sizes during the 1990s may reflect new policies and legislation. Subsequent stability in sample size could be due to modern trial designs (eg, basket/umbrella studies, surrogate endpoints) that require smaller sample sizes. The recent decrease in protocol sample sizes may herald a similar decline for future studies but requires post-market surveillance to verify credibility.