Evaluation of Factors Associated with Recruitment in Breast Cancer Clinical Trials in a Specialized Breast Cancer Centre.
Bibliographic record
Abstract
Abstract Background: Recruitment of patients (pts) in cancer clinical trials has been reported to be between 3-5%. Very few data come from Canada. Methods: The objective was to measure the recruitment and its associated characteristics in breast cancer clinical trials for non-metastatic breast cancer pts. This was a retrospective cohort study at the Centre des Maladies du Sein Deschênes-Fabia, a specialized breast cancer centre in Québec City, Canada. Clinical trials opened between 2004 and 2008 were used. For each protocol, main criteria were used to define the population under study (e.g. triple negative breast cancer). Charts were identified from our database and reviewed in decreasing chronological order of diagnosis. Staging, hormone receptors status and Her2 status were available from the database. All charts were reviewed to assess eligibility criteria (inclusion and exclusion criteria). Also, information was sought in the notes about whether or not the protocol has been proposed to the pt, if the pt accepted and if not, reason(s) for refusal. Protocol and physicians characteristics were collected. Results: A total of 9 protocols were studied and 244 charts identified (relates to 704 pt-protocols because one pt could be assessed for more than one protocol if applicable). Eligibility could not be assessed for 19% (n=138) pt-protocols. Among all pt-protocols, 74.4% met all the eligibility criteria. There was a note in the chart, about protocol being proposed to a potentially eligible pt, in 13.8% of the pt-protocols. Overall, 9.7% of pt-protocols were recruited. Among patients who had a protocol offered, 74.2% accepted. Data on characteristics associated with recruitment will be presented at the meeting. Conclusion: Enrollment in clinical trials in a specialized breast cancer centre is slightly higher than the 3-5% generally reported but still low. Interventions should focus on finding ways to offer a clinical trial when available to every suitable eligible patient. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 5122.
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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 | 0.060 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".