Patient and oncologist preferences for ALK+ advanced non-small cell lung cancer tyrosine kinase inhibitor treatments: a discrete choice experiment in the United States
Bibliographic record
Abstract
PURPOSE: Next-generation anaplastic lymphoma kinase (ALK)-targeting tyrosine kinase inhibitors (TKIs) are standard first-line (1L) treatments for ALK+ advanced non-small cell lung cancer (aNSCLC). Treatments differ in systemic and central nervous system (CNS) efficacy and adverse event (AE) profiles. There is a need for understanding treatment preferences of patients and oncologists in this setting. PATIENTS AND METHODS: Patients receiving TKIs for ALK+ aNSCLC and oncologists were recruited from patient databases, patient advocacy groups and an online panel to complete a discrete choice experiment, which included progression-free survival (PFS), brain metastases (BM) development, BM progression, metabolic events, weight gain, CNS AEs, fatigue/asthenia, and muscle/bone pain. Responses were analyzed using a mixed logit model. Relative attribute importance (RAI), minimum acceptable benefit, and maximal acceptable risk were calculated. RESULTS: Of the 151 patients, 23.2 % had BM and 50.3 % were on 1L treatment. Treatment benefits outweighed AEs and contributed to 73.6 % of patients' and 67.0 % of oncologists' total RAI. Stopping BM progression was most important to patients (27.2 %), whereas PFS was most important to oncologists (31.1 %). Oncologists placed two and four times as much importance on avoiding CNS AEs and metabolic events, respectively, than patients. Patients placed more importance on avoiding fatigue/asthenia than oncologists. CONCLUSIONS: To our knowledge, this was the first study to quantify preferences regarding 1L treatments for ALK+ aNSCLC in the US. As patients and oncologists were shown to have different priorities, understanding the differing trade-offs between treatment benefits and AEs can facilitate shared decision-making and personalized 1L treatment for ALK+ aNSCLC.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".