Understanding participation in a trial comparing cryotherapy and radiation treatment.
Bibliographic record
Abstract
BACKGROUND: To date, few two-arm active treatment randomized control trials (RCTs) have compared prostate cancer therapies. OBJECTIVE: To examine the difference and similarities between the reasons for accepting and declining participation in a two-arm active treatment RCT comparing external beam radiation therapy (EBRT) versus cryotherapy. METHODS: Eleven men with prostate cancer, selected purposively, participated in a 30-minute post-treatment semi-structured interview. Interviews were transcribed verbatim, coded and analyzed for patterns with the assistance of the text management (TM) software (NVivo). RESULTS: RCT accepters participated principally with the hope of being randomized into the cryotherapy treatment arm. Consequently, randomization into the EBRT arm was often perceived as receiving the consolation prize. RCT decliners were either pushed away from cryotherapy and/or pulled towards another treatment (surgery, EBRT, brachytherapy). Factors influencing accepters'/decliners' treatment decisions include (1) personal factors such as patient research and treatment preference, cancer survivors, family/friends, and altruism, and (2) physician, trial, and treatment factors such as patient-physician rapport, RCT awareness and understanding, therapy convenience, expected outcome and perceived side effects. CONCLUSIONS: By better understanding patients' views about RCT participation, recruitment rates for prostate cancer RCTs can be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.121 | 0.273 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".