Patients’ considerations of time toxicity when assessing cancer treatments with marginal benefit
Bibliographic record
Abstract
BACKGROUND: Effective techniques for eliciting patients' preferences regarding their own care, when treatment options offer marginal gains and different risks, is an important clinical need. We sought to evaluate the association between patients' considerations of the time burdens of care ("time toxicity") with decisions about hypothetical treatment options. METHODS: We conducted a secondary analysis of a multicenter, mixed-methods study that evaluated patients' attitudes and preferences toward palliative-intent cancer treatments that delayed imaging progression-free survival (PFS) but did not improve overall survival (OS). We classified participants based on if they spontaneously volunteered one or more consideration of time burdens during qualitative interviews after treatment trade-off exercises. We compared the percentage of participants who opted for treatments with no PFS gain, some PFS gain, or who declined treatment regardless of PFS gain (in the absence of OS benefit). We conducted narrative analysis of themes related to time burdens. RESULTS: The study cohort included 100 participants with advanced cancer (55% women, 63% age > 60 years, 38% with gastrointestinal cancer, and 80% currently receiving cancer-directed treatment. Forty-six percent (46/100) spontaneously described time burdens as a factor they considered in making treatment decisions. Participants who mentioned time (vs not) had higher thresholds for PFS gains required for choosing additional treatments (P value .004). Participants who mentioned time were more likely to decline treatments with no OS benefit irrespective of the magnitude of PFS benefit (65%, vs 31%). On qualitative analysis, we found that time burdens are influenced by several treatment-related factors and have broad-ranging impact, and illustrate how patients' experiences with time burdens and their preferences regarding time influence their decisions. CONCLUSIONS: Almost half of participating patients spontaneously raised the issue of time burdens of cancer care when making hypothetical treatment decisions. These patients had notable differences in treatment preferences compared to those who did not mention considerations of time. Decision science researchers and clinicians should consider time burdens as an important attribute in research and in clinic.
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.
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 | 0.033 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".