Understanding the attitudes of the elderly towards enrolment into cancer clinical trials
Bibliographic record
Abstract
BACKGROUND: The optimal cancer treatment for an older population is largely unknown because of the low numbers of elderly patients accrued into clinical trials. This project focuses on the attitudes of the elderly about participation in clinical trials to determine if this is one of the barriers to the involvement of this population in clinical trials. METHODS: The first phase of this study was a self-administered questionnaire mailed to 425 elderly persons with cancer, selected from Princess Margaret Hospital oncology clinics. The second phase consisted of individual semi-structured interviews with cancer patients to assess their attitudes towards cancer, its management and enrolment into cancer clinical trials. RESULTS: Ninety-four patients responded to the survey giving a response rate of 22.1%. Three quarters of respondents stated that they would be willing to participate in a clinical trial. The factors that most influenced older patients' willingness to participate in a cancer study were recommendations from a cancer doctor and the chance that the study treatment may help them feel better. Seventeen survey responders participated in interviews. Common themes from these interviews included patient-physician communication, the referral process, and the role of age in cancer care decision-making. CONCLUSION: Most elderly people, who responded to this survey, are willing to consider participation in cancer clinical trials however, elderly patients do not appear to actively seek clinical trials and few were informed of the availability of clinical trials. Physician barriers and availability of appropriate clinical trials may play a bigger role in preventing accrual of elderly cancer patients into trials.
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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 | 0.019 | 0.054 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".