Evaluation of adolescents and young adults (AYA) attitudes towards participation in cancer clinical trials.
Bibliographic record
Abstract
10047 Background: Participation in clinical trials (CT) for AYA ( < 39 years) remain the lowest of any patient group with cancer. Little is known about the personal barriers to AYA accrual. The aim of this study was to explore AYA attitudes that influence CT participation. Methods: A mixed methods approach included 1) qualitative: interpretive descriptive methodology guided individual semi-structured interviews with 21 AYA for factors influencing CT enrollment and 2) quantitative: AYA and non-AYA (≥40) matched for histology completed Cancer Treatment subscale of Attitudes toward Cancer Trials Scales (ACTS-CT) (Schuber, 2008) and 9 supplementary questions formed from interview analysis. Differences between AYA and non-AYA cohorts were analyzed using the Mann-Whitney U test and ordered logistic regression models were constructed for prediction of the effect of baseline demographics. Results: The major themes influencing CT participation were: (1) family/peer group opinion (2) CT impact on daily/future life (e.g. school; starting a family) and (3) illness severity/psychological readiness for CT information. Surveys were distributed to 61 AYA (median age: 29 years (17-39)); 74 non-AYA (55 (40-88)). Compared with non-AYA, AYA perceived CT to be unsafe/more difficult (Personal Barrier/Safety domain; p = 0.01). AYA were also more concerned with CT interference in their long term goals (p = 0.04). Logistic regression identified participants who had previously been offered a CT (p = 0.01) or who spoke English as their first language (80% of cohort)(p = 0.01) reported less barriers to CT. There were no differences based on age in other domains (Personal Benefits; Personal/Social Value; Trust in CT). In all participants, differences were seen in the Personal Benefits domain if respondents had children (p = 0.05) or were currently working (p = 0.04). Conclusions: Age-related differences in attitudes towards CT suggest that tailored approaches to CT accrual of different patient groups may be warranted. Patient-centered delivery of information regarding CT, particularly for those in whom English is a second language and who are trial-naïve, may improve accrual and warrants further prospective, randomized study.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".