Advancing Global Cancer Symptom Science: Insights and Strategies from the Inaugural Cancer Symptom Science Expert Meeting
Bibliographic record
Abstract
BACKGROUND: The inaugural "Cancer Symptom Science Expert Meeting," held in Lausanne, Switzerland, on October 11-12, 2023, brought together 40 nurse scientists from seven countries to enhance collaboration across the global symptom science community; identify common research interests, gaps in knowledge, and opportunities for research; and develop strategies to address challenges and accelerate symptom science research internationally. OBJECTIVES: The aim of this white paper were to summarize the discussions and recommendations deliberated during the meeting and introduce the Global Research Alliance in Symptom Science (GRASS). METHODS: This 2-day meeting featured presentations that highlighted critical issues and unanswered questions in cancer symptom science and other chronic conditions. Attendees identified four core topic areas based on the knowledge gaps reflected throughout the presentations. Four working groups (WGs) were formed to identify gaps and opportunities associated with each topic and to outline strategic directions and essential actions to advance symptom science. RESULTS: The WGs developed recommendations on four core topic areas. WG1 explored optimal approaches to collect, analyze, and use symptom data for research and clinical purposes. WG2 addressed the development of a minimum dataset or common data model for symptom science research. WG3 focused on enhancement of best practices in implementation science strategies to improve uptake of evidence-based symptom management strategies in routine clinical care. WG4 addressed the questions of capacity building and infrastructure for the creation of a global alliance in symptom science (GRASS). DISCUSSION: WGs' recommendations underscore the commitment of an international coalition of scientists to advance symptom science. The symposium established the groundwork for the group to constitute GRASS, a global research alliance dedicated to symptom science in cancer and other chronic conditions. Future directions include establishing regular scientific meetings, fostering interdisciplinary collaboration, and engaging with symptom scientists.
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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 | 0.180 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.005 | 0.039 |
| Research integrity | 0.019 | 0.034 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".