The experience and needs of myeloma patients: exploring race and ethnicity
Bibliographic record
Abstract
Importance: Race and ethnicity are critical factors that influence healthcare equity for cancer patients and need to be studied. Objective: This study explores how race and ethnicity influence patients living with multiple myeloma (MM), an incurable blood cancer. Design: Four hundred and thirty-five patients diagnosed with smoldering or active MM completed an online survey. Measure: The survey consisted of questions related to MM diagnosis, patient needs at diagnosis and later in treatment, and interest in coaching with an experienced MM patient/caregiver. Results: Results revealed differences among Black, White, and MHL (Mexican, Hispanic, and Latino/a) patients. Black patients, in this study, had a higher genetic risk for MM compared to White and MHL patients. However, these differences did not reach statistical significance. Black patients were also the least likely to be seeing a MM specialist. Similarities regarding needs at diagnosis included treatment options, life expectancy, and basic information about MM. All participants across identities reported an interest in clinical trials. Black patients more frequently reported wanting to know how to cope with anxiety and fear. Black patients also more frequently expressed interest in receiving one-on-one patient centered coaching than White and MHL patients. MHL patients were the least interested in coaching. Conclusion: There are differences and similarities across racial and ethnic identities, and a clear need for more outreach to Black patients with MM who have higher risk diagnoses; less care from MM specialists; and are interested in coaching, clinical trials, and receiving support.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".