EMPOWER Retinoblastoma: Engaging Patient Partners in Solving the Top 10 Priorities for Eye Cancer Research in Canada
Bibliographic record
Abstract
While it is recognized that research priorities should reflect and integrate the perspectives and needs of patients along with those of health professionals and researchers, it remains challenging to actualize such priorities into tangible research projects. Targeted dissemination is required to catalyze research on these priorities. To create awareness of and inspire action toward actualizing the top 10 retinoblastoma research priorities in Canada, Canadian Retinoblastoma Research Advisory Board (CRRAB) members developed a wide range of dissemination tools and processes. These resources, co-produced with patients, were instrumental to CRRAB sharing the top 10 priorities internationally to mobilize action toward solving them.
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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 | 0.045 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".