P1‐530: RECRUITMENT STRATEGIES OF PARTICIPANTS WITH MCI: THE EFFECTIVENESS OF FREE MEMORY SCREENING
Bibliographic record
Abstract
Meeting recruitment and enrollment objectives for longitudinal studies has been a continuing struggle for clinical researchers. Many obstacles hinder enrollment of potential participants such as degree of interest, health exclusions and unwillingness to participate in study procedures. To gain a better perspective of and to adapt to these challenges, we have assessed what proportion of the community that participates in memory screening events enroll in a clinical study. The present study examines the demographic of the last 2 years of recruitment events and factors that play a role in enrollment success. Memory screenings were scheduled quarterly in both 2017 and 2018 at the UCSD Shiley-Marcos Alzheimer's Disease Research Center. Events were advertised through the newspaper in January of each year. Ten exam rooms were reserved for 30-minute appointments and a conference room was available as a resource for participants to gain access to support materials. Undergraduate and graduate students who received rigorous training administered Story A from the Logical Memory subtest of the Wechsler Memory Scale-Revised and the Montreal Cognitive Assessment (MoCA) in English (n=555). The Mini-Mental State Exam and Consortium to Establish a Registry for Alzheimer's Disease were performed on Spanish-speaking participants (n=19). Within the resource room, staff reviewed screening results, determined from appropriate test normative data, with participants and provided information regarding research studies to those interested. 574 people (overall age= 74.4 years; education= 16.1 years; MoCA= 23.8) were screened at six memory screening events over the course of 2017 and 2018. Of the total of individuals screened, 378 participants consented to be a part of our registry database. From the total consented, 80 have enrolled in our longitudinal study.
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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 | 0.073 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".