Examining factors affecting clinical trial enrolment in the Clinical Trials Navigator program.
Bibliographic record
Abstract
e23143 Background: Oncology clinical trial accrual rates are estimated to be around 5%, despite the potential benefits patients could receive on a clinical trial. Many factors contribute to whether a patient is successfully enrolled onto a clinical trial. The Clinical Trials Navigator (CTN) Program helps oncology patients identify clinical trials. We analyzed program and patient characteristics to determine features of successful enrollment. Methods: A retrospective study was conducted. From March 2019 to April 2024, 411 records from the CTN program were analyzed. Of the 411, 73 were referred to a clinical trial. 14 of the 73 were enrolled onto a trial. The characteristics evaluated for the 14 enrolled and the 59 non-enrolled patients were: age, distance from home center to clinical trial site, CTN processing time, and time of initial CTN application to death. For the non-enrolled, the reason for non-enrollment was recorded. For the enrolled, the type of trial, trial phase and discipline were recorded. All comparative values were analyzed using a Welch’s T-test. Results: Comparison of the data between the enrolled and non-enrolled revealed that the average age was similar between both groups with the enrolled being 61 years, and non-enrolled being 57 years ( p = 0.154). The mean distance from home center to clinical trial site was 332.9 kilometers (km) for enrolled and 407.6 km for non-enrolled ( p = 0.152). The CTN processing time for the enrolled group had a mean time of 4.1 days and the non-enrolled had a mean time of 12.5 days ( p = 0.002). The time of initial CTN application to death for the enrolled group had a mean of 17.4 months and the non-enrolled had a mean 7.9 months. ( p = 0.0051). For the non-enrolled group, the reason for non-enrollment was centre specific (60.1%) or patient specific (39.9%). The centre specific reasons included: non-eligibility (45%), trial no longer available (31%), centre declined (16%), COVID-19 delays (5%), not accepting patients (3%). The patient specific reasons included, the patient: passed away during the process (48%), sought alternative treatment (19%), declined (24%), lost to follow up (9%). For the enrolled group, the trial types were interventional (71%) and next generation sequencing (NGS) (29%). Enrollment of patients by phase of trial were as followed: three phase I (21.4%), two phase I/II (14.3%), three phase II (21.4%), one phase II/III (7.1%), one phase III (7.1%), and four NGS (28.6%). Conclusions: These findings highlight some of the unique barriers and opportunities for patients in patient-centered clinical trials enrollment. The importance of the efficiency of the CTN is highlighted. Almost one-third of patients were enrolled in phase I or phase I/II trials, demonstrating patient’s willingness to travel for early phase trials. Early referral in the patient journey, inclusion of all phases of trials and efficient patient processing will lead to higher clinical trials accrual.
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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 | 0.024 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".