A multi-stakeholder approach in optimising patients’ needs in the benefit assessment process of new metastatic breast cancer treatments
Bibliographic record
Abstract
There is a growing understanding as science evolves that different cancer types require different approaches to treatment evaluation, especially in the metastatic stages. The introduction of new metastatic breast cancer (MBC) treatments may be hindered by several elements, including the availability of relevant evidence related to disease-specific outcomes, the benefit assessment process around the evaluation of the clinical benefit and the patients' need of new treatments. The Steering Committee (SC) found that not all issues relevant to MBC patients are consistently considered in the current benefit assessment process of new treatments. Among these are overall survival, time-to-event endpoints (e.g. progression-free survival), patients' priorities, burden of disease, MBC-specific quality of life, value in delaying chemotherapy, route of administration, side effects and toxicities, treatment adherence and the benefit of real-world evidence. This paper calls on decision makers to (1) Include MBC-specific patient priorities and outcomes in the overall benefit assessments of new MBC treatments; (2) Enhance multi-stakeholder collaboration in order to improve MBC patient outcomes.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".