Patient Priorities Concerning Treatment Decisions for Advanced Neuroendocrine Tumors Identified by Discrete Choice Experiments
Bibliographic record
Abstract
BACKGROUND: Patients with advanced neuroendocrine tumors (NETs) have multiple treatment options. Ideally, treatment decisions are shared between physician and patient; however, previous studies suggest that oncologists and patients place different value on treatment attributes such as adverse event (AE) rates. High-quality information on NET patient treatment preferences may facilitate patient-centered decision making by helping clinicians understand patient priorities. METHODS: This study used 2 discrete choice experiments (DCE) to elicit preferences of NET patients regarding advanced midgut and pancreatic NET (pNET) treatments. The DCEs used the "potentially all pairwise rankings of all possible alternatives" (PAPRIKA) method. The primary objective was to determine relative utility rankings for treatment attributes, including progression-free survival (PFS), treatment modality, and AE rates. Ranking of attribute profiles matching specific treatments was also determined. Levels for treatment attributes were obtained from randomized clinical trial data of NET treatments. RESULTS: One hundred and 10 participants completed the midgut NET DCE, and 132 completed the pNET DCE. Longer PFS was the highest ranked treatment attribute in 64.5% of participants in the midgut NET DCE, and in 59% in the pNET DCE. Approximately, 40% of participants in both scenarios prioritized lower AE rates or less invasive treatment modalities over PFS. Ranking of treatment profiles in the midgut NET scenario identified 60.9% of participants favoring peptide receptor radionuclide therapy (PRRT), and 30.0% somatostatin analogue dose escalation. CONCLUSION: NET patients have heterogeneous priorities when choosing between treatment options based on the results of 2 independent DCEs. These results highlight the importance of shared decision making for NET patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".