A Model for Community‐based Recruitment for Dementia Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Successful enrollment of participants into clinical dementia trials remains one of the more challenging aspects of clinical research, as barriers exist for both potential participants and recruiting clinicians. Currently, a mechanism is lacking to engage the community. METHOD: The Toronto Dementia Research Alliance (TDRA) and the Alzheimer Society of Toronto (AST) co-developed a web recruitment platform, Toronto Dementia Network (TDN) (https://tdn.alz.to/research-studies/) to enable persons living with dementia, their caregivers, and healthy volunteers, to discover trials and to connect clinicians to otherwise inaccessible participants. The platform features plain-language summaries of investigator-initiated clinical trials. Potential participants can perform key-word searches, use predetermined filters, or fill out a questionnaire to be matched to a suitable trial by TDRA staff. Their contact information is securely captured and shared with the site leading the trial of interest. Once transferred, staff leading that trial contact the individual who made the request. This triage system is tracked and monitored by TDRA. This platform is featured in TDRA's monthly newsletter and through a bi-monthly webinar series. RESULT: 2025, 60 studies have been registered on the TDN site. 266 potential participants have demonstrated interest. Overall, this method has led to 34% of participant referrals becoming enrolled in a study. Specifically, 36% of referrals via TDRA-assisted screening and study matching have led to enrollments/study completions, while 30% of participant self-referrals have led to enrollments/study completions - higher than traditional recruitment methods. CONCLUSION: A patient-friendly web-based approach for participation into clinical trials is possible. We have demonstrated that such an approach can yield over 30% recruitment. In addition, having a research team dedicated to screening, triaging and matching participants to appropriate studies results in a higher enrollment rate than self-referrals.
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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 | 0.387 | 0.404 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.050 | 0.025 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".