Identification of cancer care and protocol characteristics associated with recruitment rate in breast cancer clinical trials in Ontario
Bibliographic record
Abstract
17042 Background: Recruitment rate (RR) in clinical trials (CT) has been recognized to be low. Poor accrual may lead to premature closing of CT or decrease of the planned power to detect an effect if present. Methods: Objectives were primarily to identify characteristics of cancer care settings and clinical trials protocols associated with low RR and secondarily (1) to determine the RR and (2) to compare the RR between years. A cross-sectional design was used. Poisson regression was used for multivariate analysis. RR was calculated by CT, hospital and year in Ontario between 1997 and 2002. Number of patients recruited in each CT was obtained from cooperative groups and pharmaceutical companies. Number of patients with breast cancer (BC) was obtained from the Ontario Cancer Registry. Prevalence of women with metastatic BC was calculated from the British Columbia Breast Cancer Outcome Unit database. Characteristics of cancer care and protocols were collected. Results: Response rates were 84% (66/79) for hospitals, 69% (9/13) for cooperative groups and 80% (8/10) for pharmaceutical companies. Recruitment rates varied between 1.3% and 5.5% (median, p=0.0003). Characteristics of cancer care were not associated with RR (number of oncologists, breast oncologists, breast surgeons, investigators, clinical research associates and being a cancer centre or an academic centre). Among protocol characteristics, the following were associated with RR in univariate analysis: phase, randomization, type of intervention, placebo, extent of the trials (local vs. national vs. international), number of sites, population (adjuvant vs. metastatic), menopausal status, premature closing of the trial, time frame for enrolment, extra baseline and follow-up testing. In multivariate analysis, type of control arm and time frame for enrolment were significant. CT using placebo compared to an active control arm were less likely to recruit patients (relative risk 0.57, p=0.0144). CT with a time frame for enrolment greater than 9 weeks were more likely to enrol patients (relative risk 1.43, p=0.0020). Conclusions: RR is very low. No easily modifiable factors have been identified. This project was funded by the Canadian Breast Cancer Foundation, Ontario Chapter. [Table: see text]
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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 | 0.098 | 0.248 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".