212 Research Priorities for Cancers of the Oesophagus and Stomach: Recommendations from a United Kingdom & Ireland Patient and Healthcare Professional Partnership Exercise
Bibliographic record
Abstract
Abstract Aim Oesophagogastric (OG) cancers are a major cause of morbidity and mortality. Research is crucial to improving outcomes but to maximise value and impact, areas of focus should be prioritised in partnership with patients. Method We undertook the first comprehensive analysis of patient and healthcare professional (HCP) priorities for research across the domains of prevention, diagnosis and staging, treatment, palliative care and survivorship. An initial scoping survey sought research uncertainties from HCPs and patients. These were consolidated into true research uncertainties, each confirmed by systematic review, and their potential impact scored by HCPs. A domain-specific weighting reflecting patient values was then applied to prioritise identified uncertainties. Results In total, 835 (395 HCP, 440 patient) responses were received, with 3906 suggested priorities consolidated to 92 true research uncertainties. HCP respondents represented 19 community and hospital professions and specialties involved in OG cancer care. Across the domains, patient weighting changed 22.2%-46.3% of the priority rankings established by HCP scoring. There was a high degree of agreement between individual HCPs as well as between HCPs and patients for the highest-ranked research uncertainties. These focused on selecting those who should be screened, identifying causes for late diagnosis, determining the most effective treatment combinations, optimizing nutrition across multiple settings and evaluating the long-term impact of prehabilitation. Conclusions This work highlights the impact of patient input on HCP-ranked research priorities and provides a robust list of priorities to guide funders, policy makers and researchers to support and undertake impactful research focused on OG cancer.
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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 | 0.001 | 0.001 |
| 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".