P1‐422: Telehealth‐Enhanced Collaborative Geriatric Care (Protec): Evaluation of Cognitive Assessment Consultation Protocol of Rural‐Dwelling Older Veterans
Bibliographic record
Abstract
Older rural veterans account for a significant proportion of VA health care costs (West & Weeks 2009). Cognitive impairment is increasingly prevalent among older adults. The lack of specialty services in rural settings is a significant barrier in addressing cognitive syndromes and providing quality care. Rural dwelling Veterans were identified and screened for memory deficits with the Montreal Cognitive Assessment (MoCA). Scores in the impaired range were referred for formal neurological evaluation. This evaluation included the neurological, health education and social work assessments. Participants were screened with the Risk Assessment tool, which assesses six areas: legal/financial, safety, physical debility and falls, mood and unnecessary functional decline, social isolation, and care crises. This analysis evaluates 301 clients. The majority (95%, n=285) of the clients were male. Age ranged from 24 to 94 with the average age of 70. (See table 1). The most common diagnosis is mild cognitive impairment (MCI-29), followed by depression (25), Alzheimer’s disease (AD-13), post-traumatic stress disorder (PTSD-10), and traumatic brain injury (TBI-8). (See table 2). Some key findings include risk reductions on financial risks, 67% at baseline and 75% on follow-up, completed advanced health directives increased from 69% to 85% at the last follow-up. Most were not driving despite their disability (86%) but this increased to almost all (95%) at follow-up. The knowledgeable about the disease increased from 56% to 84%. (See table 3). A full summary of results will be included on the poster.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.008 |
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".