A National UK Survey of Multiple Myeloma Patient Led Research Priorities: Embedding PROMs, Patient Characteristics and Patient Perspectives
Bibliographic record
Abstract
Background James Lind Alliance (JLA) and Myeloma Canada published a set of myeloma research priorities through international consultation with Multiple Myeloma (MM) patients and carers, alongside healthcare professionals and key stakeholders. Whilst these priorities steer international research, MM is a heterogeneous cancer that manifests uniquely in each patient, resulting in the potential for diverse preferences and priorities for research across the population. Developing priorities that are shaped by the lived experiences and characteristics of patients should redefine the list and drive forward a more meaningful discourse on research in MM and areas of unmet need. This study sought to address this gap and consider how Patient Reported Outcomes Measures (PROMs), patient characteristics and their perspectives shape research priorities that are specific to sub-populations of MM patients. Methods MM patients participated in a national survey in the UK and were presented with an adapted list of the JLA priorities based on feedback from patients on the Myeloma UK Patient and Carer Research Panel. In the main survey, participants were presented with 10 priorities and asked to rank each in order of importance from 1 highest to 10 lowest priority. Patients were then asked to provide an explanation for selecting their top three priorities. Participants completed the PROMIS-29 v2.0 HRQoL, Decisional Self-Efficacy (DSE), Decisional Regret, Cancer Locus of Control scales, and reported the number of symptoms they experienced at diagnosis, side-effects in response to their most recent treatment, and whether they received a timely (<3 months from initial symptoms) or a delayed (3+ months from initial symptoms) diagnosis. Patient demographics were also collected. After scoring the PROMs and rank ordering the priorities, a multiple regression was applied to the data to establish the influence of PROMs and patient characteristics on the relative position of the priorities. Qualitative data was analyzed using framework analysis. Results 818 patients participated; 514 completed the survey. Overall, MM patients ranked: 1. finding a cure, 2. finding new treatments for myeloma patients to improve their life-expectancy, and 3. providing patients with treatments that target their type of myeloma (based on what we know about a patient and their genes), as their top 3 priorities. The rank order of the 10 priorities was influenced by several PROMs. For example, the position of the priority 'ensuring myeloma is diagnosed more quickly' (ranked 4th in our list) was elevated to a higher position when delayed diagnoses, symptom burden at diagnosis, and experiencing symptoms that negatively impact HRQoL were considered. The position of 'finding new treatments for myeloma patients to improve their life expectancy' (ranked 2nd in our list and below the top priority 'finding a cure') became elevated to a higher position when levels of anxiety and symptoms that negatively impact HRQoL were considered. Furthermore, the position of 'reducing long-term side effects of myeloma treatments' (ranked 10th in our list), was elevated when patients who perceived themselves as having more control over the cause of their disease was considered. Patient characteristics also influenced the rank order of these priorities. Characteristics such as gender, employment status and the geographical location of patients influenced the relative position of 'ensuring myeloma is diagnosed more quickly' and elevated this priority to a higher position (previously ranked 4th in our list). 'Understanding if new treatments, that use the body's immune system, can fight cancer, e.g. CAR-T', previously ranked 8th on our list, was elevated to a higher position when unemployment was considered. Framework analysis provided further depth on the perspectives of patients and their decision making. For example, MM patients viewed the priority 'finding a cure' as an opportunity to prevent rather than 'cure' MM, an important distinction in the development of research programs for precursor conditions. Conclusions Our study acts as a driver for future UK research programs by developing a list of priorities that are tailored to sub-populations and the lived experiences of MM patients. The results have implications for the direction of travel of future scientific programs in MM and alignment of research to the unmet needs and personal circumstances of patients.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".