Altruism among participants in cancer clinical trials
Bibliographic record
Abstract
BACKGROUND: Patients' motivations for participation in cancer clinical trials are incompletely understood. Even less is known about the factors that influence participants' motivations for enrolling in trials. PURPOSE: We studied the reasons why adult patients and parents of pediatric patients agree to participate in cancer trials. We focused on the role of altruism across all phases of trial. METHODS: We surveyed adult patients and parents of pediatric patients participating in phase I, II, or III cancer clinical trials. We asked respondents why they agreed to enroll, and examined correlates of altruistic motivation using univariate and multivariate analyses. RESULTS: Among 205 adults and 48 parents of children participating in cancer trials, 47% reported that altruistic motivations were 'very important' to their decisions to enroll. In multivariate analysis with phase III trial participants as the reference group, phase I trial participants least often identified altruism as a 'very important' motivation for enrolling (phase I OR 0.4, 95% CI (confidence interval) 0.2-0.8; phase II OR 0.9, 95% CI 0.5-1.5, overall P = 0.017). Thirty-three respondents (13%) reported being motivated primarily by altruism. In multivariate analysis, participants with poor prognoses-defined as an expected 5-year disease-free survival of ≤ 10%-reported altruism as their primary motivation less often than those with better prognoses (OR 0.2, 95% CI 0.1-0.5, P = 0.001). Altruistic motivations did not differ between adult patients and parents of pediatric participants. LIMITATIONS: The data are derived from related academic medical centers in one city, and the study sample reflects limited sociodemographic diversity, thereby limiting generalizability to other settings. CONCLUSIONS: Although cancer trial participants commonly report that altruism contributed to their decision to enroll, it is rarely their primary motivation for study participation. Participants in early phase trials and those with poor prognoses are least often motivated by altruism.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".