Enhancing the patient journey to clinical trial enrollment with navigation to optimize accrual: A pilot study for a pragmatic multicentre, stepped wedge, cluster randomized controlled trial (The CTN Pilot Trial).
Bibliographic record
Abstract
11093 Background: Clinical trials are essential to the advancement of clinical therapies, yet accrual rates remain disappointing. Multiple challenges lead to less than 5% of cancer patients enrolled onto clinical trials. The Clinical Trials Navigator (CTN) program was established to assist patients and health care professionals identify appropriate clinical trials for patients. Methods: Between March 2019 to January 2024, a novel navigator-assisted clinical trials search program was offered to Canadian patients. Three non-medical navigators were trained to receive referrals, review medical information, and search five different clinical trial search engines. Eligibility criteria was scrutinized. A second review of the clinical trial list was conducted by two physicians. The final curated list of clinical trials was provided to patients and their oncologist. Results: A total of 373 patients were referred to the CTN program during the study period. A unique clinical trial search was performed for each patient yielding a median of only one potentially eligible trial per patient. Clinical trial enrolment occurred in 3.2% of patients in our database which translates to a 19% rate of successful enrolment of those referred to a trial by the CTN. Most patients (78%) were referred to clinical trial sites that conducted more than 100 clinical trials at any time. Compared to the Canadian cancer statistics, lung, lymphoma, pancreatic and brain cancers were overrepresented in referrals to the CTN program while prostate cancer was underrepresented. Type of cancer played a significant role in the likelihood of a successful referral (p < 0.01). Lung cancer was the most frequently reported cancer that resulted in referrals and breast cancer showed a lower frequency of referrals. The cancer type, stage and number of lines of prior therapy were not significantly associated with the patient enrollment onto a clinical trial. An increase in survival of referred patients from last analysis from 3.0 months to 5.3 months. Conclusions: The CTN program is a successful tool to identify clinical trials for cancer patients and can improve clinical trial accrual, as almost one fifth of patients (19%) who were referred to a clinical trial were enrolled. Ongoing iterative changes to the program to improve these metrics are underway and efforts to improve implementation of the CTN program across Canada are ongoing.
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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 | 0.044 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".