The case‐finding study: A novel community‐based research recruitment approach for engaging participants with early cognitive decline
Bibliographic record
Abstract
INTRODUCTION: Innovative recruitment strategies are needed to better engage potential research participants at a preclinical stage of cognitive decline. METHODS: Local newspaper advertisements attracted community-dwelling people ≥55 years with memory concerns, who were interested in research, to self-refer for cognitive assessment and discuss cognitive research involvement. Respondents completed telephone screening and then attended an in-person cognitive screening assessment with a study partner. Case conferencing with a clinician researcher characterized a "clinical suspicion" of the participant's cognitive concern. RESULTS: Of 209 respondents who underwent in-person assessment, 203 participants were classified as having subjective cognitive decline (47%), mild cognitive impairment (44%), or dementia (9%). Thirty percent of participants were enrolled in observational studies or randomized controlled trials. DISCUSSION: Community-based engagement, cognitive screening, and case conferencing effectively combined to identify research participants at risk of cognitive decline and recruited participants into cognitive research studies. Those not recruited continued to be followed up longitudinally.
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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 | 0.065 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".