How Important is Healthcare-Contact Time to Systemic Treatment Decision-Making in Advanced Gastrointestinal Cancers: Developing Attributes to Include in a Discrete Choice Experiment
Bibliographic record
Abstract
BACKGROUND: People receiving treatment for advanced cancer invest substantial portions of their survival time receiving healthcare, labelled the 'time toxicity' of treatment. Although qualitative research has examined the impact of time burden on patients and their caregivers, its influence on treatment decision-making is unclear. OBJECTIVE: Our objective was to explore treatment decision-making with patients with advanced gastrointestinal cancer, their caregivers, and oncologists, and unmask the role of time burden in those decisions. The objective was to inform the design of a subsequent discrete-choice experiment (DCE) investigating the importance of time burden in treatment decision-making. METHODS: A two-step process was used. Factors relevant to treatment decision-making were discussed as part of semi-structured interviews. Responses were analysed using thematic analysis with a focus on measurable themes relevant to the development of candidate attributes for a DCE. Second, we reviewed stated-preferences studies in the field of treatment decision-making in cancer and compared the results with the candidate attributes identified from interviews. RESULTS: Interviews with 45 participants (20 patients, 10 caregivers,15 gastrointestinal oncologists; 53% metropolitan) revealed 4 themes and 6 candidate attributes: expected survival benefit of treatment, impact of physical side effects, effect on day-to-day functioning, route of administration, healthcare contact days, and planned length of the treatment course. Review of 45 published studies yielded no additional attributes. CONCLUSIONS: This study identified six candidate attributes for a forthcoming DCE on time burden in advanced cancer care. These findings support growing efforts to quantify and address time toxicity in cancer treatment decision-making.
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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 | 0.080 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".