How should multiple myeloma research change in a patient-oriented world? Findings and lessons from the pan-Canadian myeloma priority setting partnership
Bibliographic record
Abstract
BACKGROUND: Over the last decade there has been considerable research into the treatment, management, and quality of life of people living with multiple myeloma. However, there has been limited investigation into topics deemed important to patients and caregivers within this community. We conducted a James Lind Alliance Priority Setting Partnership to establish the 'Top 10 Priorities for Myeloma Research', informed by patient and public partners. METHODS: A research team and steering group were established in 2019 to conduct the myeloma priority setting partnership. Steering group members included patients, caregivers, and healthcare providers who advised the research team and oversaw the scope of the project, grounded on their lived experience. Following the James Lind Alliance guidelines for identification and ranking of research questions, we used surveys and a virtual workshop to collect and prioritize questions posed by myeloma patients, caregivers, and healthcare providers across Canada. RESULTS: The Top 10 list of priorities for myeloma research was finalized at the consensus-building workshop and encompassed questions related to diagnosis, treatment, management, and living well with myeloma. A final participant evaluation survey elicited a positive response. INTERPRETATION: The myeloma priority setting partnership identified the research priorities of people living with myeloma, caregivers, and healthcare providers to inform clinical research on this disease going forward. This project underscores the importance of patient and public engagement in the identification of research questions, highlighting the concerns of people affected by myeloma to ultimately improve the lives of people living with this disease.
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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 | 0.188 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.042 | 0.018 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".