Facilitation of Enrollment onto Cancer Clinical Trials Using a Novel Navigator-Assisted Program: A Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Clinical trials are essential to the advancement of clinical therapies that improve the outcomes of people with cancer. However, enrollment in clinical trials remains a challenge. The Clinical Trial Navigator [CTN] Program was designed to address the current gap in the cancer care journey by assisting with the clinical trials search process. METHODS: survey that collected the patient's medical history. A final curated list of potential clinical trials was provided to the applicant. Metrics of success included clinical trial referral and enrollment, and we examined the factors that impacted these outcomes. RESULTS: A total of 445 people with cancer applied to the CTN program during the study. Of the 262 patients with referral and enrollment information, a trial referral occurred in 27.5% [n = 72]. Of the 72 patients who were referred to a clinical trial, 13 [18.1%] were enrolled, 9 [12.5%] are pending enrollment, and 50 [69.4%] were not enrolled. We identified a potential trial for 88% of applicants, with a median of one potential trial per patient. Physicians were highly involved as applicants. INTERPRETATION: The CTN program is successful in searching for clinical trials for people with cancer. Ongoing implementation into other Canadian sites, assessments of patient-reported outcomes, website and social media campaigns, and research into the factors that impact referral and enrollment are underway.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".