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Record W4400968014 · doi:10.1093/oncolo/oyae187

Patients’ considerations of time toxicity when assessing cancer treatments with marginal benefit

2024· article· en· W4400968014 on OpenAlexafffund
Arjun Gupta, Michael Brundage, Jacqueline Galica, Safiya Karim, Rachel Koven, Terry L. Ng, Jennifer O’Donnell, Julia tenHove, Andrew Robinson, Christopher M. Booth

Bibliographic record

VenueThe Oncologist · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsKingston General HospitalUniversity of OttawaQueen's University
FundersCanadian Institutes of Health ResearchPancreatic Cancer Action Network
KeywordsToxicityMedicineCancerOncologyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.040
GPT teacher head0.272
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2024
Admission routes2
Has abstractyes

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