Awe and anxiety for cancer cells: connecting scientists and patients in a holistic approach of metastasis research
Bibliographic record
Abstract
BACKGROUND: Metastatic cancer is often experienced by patients as a death sentence. At the same time, translational scientists approach metastasis also as an interesting phenomenon that they try to understand and prevent. These two sides of the same coin do not mask the considerable gap that exists between the laboratory world of scientists and the life world of patients. Funding agencies nowadays increasingly demand researchers to be responsive to the values and priorities of patients and public. One approach to bridge this gap and to increase the impact of science is patient and public involvement (PPI). A concise literature review of PPI research and practice in this paper revealed that although PPI is often deployed in translational health care research, its methodology is not settled, it is not sufficiently emancipatory, and its implementation in basic and translational science is lagging behind. Here, we illustrate the practical implementation of PPI in basic and translational science, namely in the context of HOUDINI, a multidisciplinary network with the ultimate goal to improve the management of metastatic disease. METHODS: This paper reports on a societal workshop that was organized to launch the holistic PPI approach of HOUDINI. During this workshop, societal partners, patients, and physicians discussed societal issues regarding cancer metastasis, and contributed to prioritization of research objectives for HOUDINI. In a later stage, the workshop results were discussed with scientists from the network to critically review its research strategy and objectives. RESULTS: Workshop participants chose the development of metastasis prediction tools, effective therapies which preserve good quality of life, and non-invasive tissue sampling methods as most important research objectives for HOUDINI. Importantly, during the discussions, mutual understanding about issues like economic feasibility of novel therapies, patient anxiety for metastases, and clear communication between stakeholders was further increased. CONCLUSIONS: In conclusion, the PPI workshop delivered valuable early-stage input and connections for HOUDINI, and may serve as example for similar basic and translational research projects.
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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 | 0.063 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.033 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".