Establishing the Top 10 Research Priorities for Adolescent and Young Adult (AYA) Cancer in Canada: A Protocol for a James Lind Alliance Priority Setting Partnership
Bibliographic record
Abstract
Adolescents and young adults (AYAs; 15-39 years) diagnosed with cancer have unique medical and psychosocial needs. These needs could be better addressed through research that is focused on the topics that matter most to them. However, there is currently no patient-oriented research agenda for AYA cancer in Canada. This manuscript describes the early development and project protocol for a priority-setting partnership (PSP) for establishing the top 10 research priorities for AYA cancer in Canada. This project follows the PSP methodology outlined by the James Lind Alliance (JLA) to engage patients, caregivers, and clinicians in research prioritization. The steps of a JLA PSP include establishing a steering group and project partners, gathering uncertainties, data processing and verifying uncertainties, interim priority setting, and a final priority setting workshop. The AYA cancer PSP will result in a top 10 list of research priorities identified by Canadian AYA patients, caregivers, and clinicians that will be published and shared broadly with the research community. The first steering group meeting was held in April 2023, and the project is ongoing. The establishment of a patient-oriented research agenda for AYA cancer will catalyze a long-term and impactful research focus and ultimately improve outcomes for AYA patients with cancer in Canada.
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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 | 0.225 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".