FEASIBILITY OF RECRUITING PEOPLE WITH MILD COGNITIVE IMPAIRMENT IN THE CONTEXT OF HEART FAILURE
Bibliographic record
Abstract
Abstract Recruiting people with mild cognitive impairment (MCI) with another chronic condition such as heart failure (HF) can be arduous. Our investigative group will discuss the challenges encountered while recruiting older adults with both MCI and HF using data from a pilot study testing the efficacy of cognitive interventions to improve cognitive function and the strategies to overcome them. Initially, eligibility criteria included age ≥65 years, HF confirmed by echocardiography, and MCI defined using a 2-step process: (1) Montreal Cognitive Assessment (MoCA) ≤23; and (2) diagnostic consensus of MCI based on the presence of cognitive impairment in the absence of functional decline. Enrollment began on 4/3/2023 by screening Cardiology and Neurology clinics patients. Only 12 participants were enrolled over the next 7 months (rate=1.5 participants/month) due to high screen failure rates (59%) owing to MoCA performances above the eligibility threshold and low recruitment rate (5%). To meet recruitment goals (8 participants/month), eligibility criteria were modified by lowering the age cutoff from 65 to 55 years and removing the MoCA screen and the MCI requirements, while adding the requirement of subjective cognitive concern allowing both those with normal cognition and MCI but not dementia. Phone recruitment was added by screening electronic health records of people who diagnosed with HF. 7 months after implementing the modifications, additional 58 participants were consented exceeding our recruitment goals (69% of those consented=MCI, 26%=normal cognition, 5%=dementia/excluded from the study). In conclusion, feasibility of our original strategies recruiting older adults with both MCI and HF was not supported.
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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 | 0.070 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".