The Role of a Patient Navigator in Reducing Health Disparities in a Distal Renal Tubular Acidosis Decentralized Clinical Trial
Bibliographic record
Abstract
Background: It is well documented that there are a multitude of health disparities that impede clinical trial enrollment in underrepresented racial and ethnic minorities. These disparities/barriers include systemic mistrust of the health system, deep-rooted fear of clinical trials, cultural and language barriers, as well as lack of education and socio-economic standing. Further compounding the complexities are decentralized clinical trials (DCTs) whereby patients may feel undervalued, unsafe, and less supported. Diverse trial participation is critical to understand therapeutic effectiveness in different populations; hence, the goal of the newly created role of industry-sponsored patient navigators will support the enriched enrollment and retention of a diverse and inclusive population in the ARENA2 trial for distal renal tubular acidosis (dRTA), a rare genetic disorder. Methods: A patient navigation team was formed of English, Spanish, and French-speaking navigators with backgrounds in social services and education. Navigators received training about dRTA, clinical trials and ARENA2. The team collaborated to ensure all patient-facing materials aligned with relative cultural standards and reflected patient-friendly, appropriately translated lay terminology. Results: The ARENA2 trial seeks to initiate enrollment June 2021 in the US and Canada. Navigators recorded native-language patient education videos about clinical trials, dRTA and ARENA2. Videos were uploaded to the ARENA2 website to inform potential subjects and establish rapport. Native-language welcome packets that included information of the dedicated services and mapped out home care visits/expectations were created. Diverse and inclusive enrollment and retention rates will be compared to prior rare disease trials upon completion of the 12-week ARENA2 study. Conclusions: Industry-sponsored, culturally sensitive, bilingual patient navigators are key stakeholders in clinical trials for rare disorders, especially DCTs, and can help reduce ethnic and racial disparities. The ARENA2 trial, will analyze and report on the patient navigator's unique role impacting trial enrollment and retention of Spanish and French speaking populations by addressing complex problems in underrepresented populations. Funding: Commercial Support - Advicenne
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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 | 0.096 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".