Enhancing Participation of Older Women in Surgical Trials
Bibliographic record
Abstract
BACKGROUND: Older participants are often excluded from clinical trials, precluding a representative sample. STUDY DESIGN: Using qualitative and quantitative methods, we examined recruitment and retention of older women with pelvic organ prolapse in two surgical trials: the randomized Colpopexy And Urinary Reduction Efforts (CARE) study and the Longitudinal Pelvic Symptoms and Patient Satisfaction After Colpocleisis cohort study. Using focus groups, we developed a questionnaire addressing factors facilitating and impeding the recruitment and retention of older study participants and administered it to research staff. Enrollment-to-surgery ratios, missed visit rates, and dropout rates for older and younger participants were compared using Fisher's exact test, with cut-points of 70 and 80 years for the CARE and Colpocleisis studies, respectively. RESULTS: Questionnaires were completed by 23 physician investigators and 11 nurses or coordinators (92% response rate). Respondents indicated it was more difficult to recruit older research participants (32%), obtain informed consent (56%), and retain participants to study completion (50%). Challenges to recruitment included caregiver involvement in the decision to participate and participant comorbidities. Perceived barriers to retention were transportation, caregiver availability, and participant fatigue. Data quality was challenged by sensory and cognitive impairment, resulting in a change from telephone interviews to in-person visits in the Colpocleisis study. Older participants did not have higher dropout rates than younger participants. There were no differences in missed in-person visits or telephone interview rates between age groups. CONCLUSIONS: Strategies, albeit unstudied, could assist investigators in planning surgical trials that successfully enroll and retain older women.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".