Disparities in clinical trial participation among older adult Medicare beneficiaries with hematologic malignancies from 2006 to 2019: A SEER–Medicare analysis
Bibliographic record
Abstract
BACKGROUND: Clinical trials (CTs) are essential for expanding treatment options across hematologic malignancies (HM) and providing access to novel treatments. However, older adults with HM are often underrepresented in CTs, and a national-level evaluation of factors influencing their participation is lacking. METHODS: The authors conducted a retrospective cohort study using the Surveillance, Epidemiology, and End Results (SEER)-Medicare database, identifying patients ≥66 years old diagnosed with HM between 2006 and 2018 (follow-up to December 2019). CT participation was defined by Medicare claims for CT services. Cumulative incidence and Fine-Gray models were used to estimate participation rates and adjusted hazard ratios (aHRs) assessed the association between participation and sociodemographic factors. RESULTS: The cohort (n = 53,919) was 50% female, median age 78 years old, and 86% White. Cumulative incidence of CT participation was low at 2.7% at 1 year after diagnosis, increasing to 4.3% at 5 years. After adjustment for the competing risk of death, significantly lower CT participation was observed for older age (vs. 66-69 years: aHR for 70-74 years, 0.79 [95% CI, 0.71-0.88]; aHR for 75-79 years, 0.63 [95% CI, 0.56-0.70]; aHR for 80-84 years, 0.41 [95% CI, 0.36-0.46]; aHR for ≥85 years, 0.21 [95% CI, 0.18-0.24]), female sex (aHR, 0.79 [95% CI, 0.73-0.86]), Black race (aHR, 0.73 [95% CI, 0.59-0.90]), certain comorbidities (aHR for pulmonary disease, 0.76 [95% CI, 0.68-0.85]; aHR for renal disease, 0.67 [95% CI, 0.59-0.76]), dual Medicare-Medicaid eligibility (aHR, 0.66 [95% CI, 0.56-0.77]), and distance to National Cancer Institute centers from the patient's ZIP code (aHR for ≥250 miles, 0.64 [95% CI, 0.48-0.86]). CONCLUSIONS: These results highlight the need for targeted interventions, such as CT navigator programs and decentralized trials, to increase older adult participation in HM CTs.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".