Physician-reported reasons for non-enrollment of older adults in cancer clinical trials.
Bibliographic record
Abstract
e18608 Background: Older adults (OA), age 65+, account for 60% of new cancer diagnoses but only 22-36% of those in clinical trials (CT). Prior studies surveyed physicians to recall reasons for not enrolling OA in general rather than assessing reasons for specific patients seen. Objectives: 1) Identify the percentage of OA vs younger adults (YA) offered CT enrollment; 2) Identify physician-reported reasons for non-enrollment of OA vs YA to CT Methods: Consecutive cancer patients (n = 503) seen in consultation at a single centre were enrolled. Patient, cancer characteristics and information about systemic therapy (type, whether accepted, and if CT was offered) were recorded from the consult note. For patients who accepted systemic therapy, but were not offered CT, medical oncologists were contacted to determine reason for not offering CT. Results were summarized using descriptive statistics. Comparison of YA and OA was done using the Chi-square or Fisher exact test for categorical variables and Wilcoxon rank sum test for continuous variables. Logistic regression was used to determine the association between age and the likelihood of being offered CT. Results: Median patient age was 66. Breast (31.2%) and gastrointestinal (25.6%) cancers were most common. Almost 40% had incurable disease. OA had more comorbidities (Charlson Comorbidity Index 2+ 24.7 vs 10%), took more medications (mean 4.2 vs 2.3), and had worse performance status (PS) (ECOG 3+ 15.1 vs 5.2%) than YA (p < 0.0001). OA were less likely to be offered systemic therapy (68.3 vs 82.1%, p < 0.001) but were as likely to accept as YA. OA were less likely to be offered a CT (14.8 vs 32.1%, p < 0.001). No available CT (75.4%), poor PS (7.8%) and ineligiblity for available CT (6.3%) were the most commonly cited reasons for not offering CT to OA and YA. Poor PS in OA was more commonly cited as a reason for not offering CT compared to YA (11.8 vs 3.9%). After adjusting for patient factors including PS and comorbidities, increasing age (by decade) was associated with a lower likelihood of being offered CT OR 0.74 (95% CI 0.6-0.9, p < 0.001). Conclusions: OA are less likely to be offered but as likely to accept systemic therapy as YA. OA are less likely to be offered CT as YA even after accounting for patient factors.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".