Connecting clinical trials with patients using patient navigation: A scoping review.
Bibliographic record
Abstract
e13560 Background: Patient navigation is a promising strategy to improve access to cancer care, but the evidence supporting its role in increasing access to cancer clinical trials has not been systematically evaluated. This scoping review aims to critically appraise, synthesize, and present the available evidence on the use of patient navigation to increase cancer clinical trial enrollment. Methods: Nine databases were searched for English peer‐reviewed articles from inception through December 21, 2023. Two independent researchers screened titles, abstracts, and full texts and extracted data using standardized forms. Results: Among the 23 included articles, 18 (78.3%) were observational studies, and only 5 (21.7%) were randomized trials. Fourteen (60.9%) were described as pilot/feasibility studies. Of the observational studies, 13 (56.5%) included a comparator group. Six (26.1%) studies were multi-institutional; 17 (73.9%) were single-center. Twenty-one (91.3%) studies were from the USA; 2 (8.7%) were from Canada. Thirteen (56.5%) focused on equity, all addressing racial/ethnic groups. Seven (30.4%) articles used patient navigation for clinical trials for all cancer types; 14 (60.9%) focused on specific cancers, with 12 (85.7%) primarily addressing breast cancer. Among 21 studies describing navigator qualifications, 4 (17.4%) required professional training (e.g., nurse, social worker), and 17 (73.9%) used community representatives. Education/training for navigators was described in 12 (52.2%) articles. The interventions used most frequently by navigators included education in 19 articles (82.6%) and care coordination in 17 articles (73.9%). Direct clinical trial referrals were unmentioned; logistical and financial assistance appeared in only 2 articles each (8.7%). Navigators in 7 (30.4%) studies directed patients to trials within and outside their center; 16 (69.6%) navigated patients only within their center. Five articles compared enrollment with and without navigation: 4 showed no improvement, and 1 reported improvement with navigation. Five other articles reported enrollment with navigation without a comparison group. Two studies limited to eligible patients reported 80.4% and 86% enrollment in a clinical trial with navigation. Three studies including all interested patients reported enrollment rates of 7%, 22%, and 22.5%. Conclusions: Evidence on patient navigation for cancer clinical trials is primarily from observational, pilot/feasibility, single-center studies in North America, with a focus on breast cancer. Furthermore, navigator training details are underreported and their interventions' scope is limited. Few studies have examined diverse equity groups. Future research should employ more rigorous designs to evaluate different patient navigation approaches and assess their impact on clinical trial enrollment across a wider range of cancers and patient populations.
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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 | 0.092 | 0.338 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.020 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".