ARAC - The Montreal Jewish General Hospital Alzheimer Risk Assessment Clinic
Bibliographic record
Abstract
INTRODUCTION: In parallel with robust efforts world-wide to develop effective neuroprotection for established disease, resources are being mobilized to delineate risk factors and implement preventive measures in a concerted effort to forestall the anticipated Alzheimer disease (AD) epidemic. A review of heritable and 'acquired' dementia risk factors, many operating at midlife, is presented in a companion paper. OBJECTIVES: In 2009, an Alzheimer Risk Assessment Clinic (ARAC) was established at the Jewish General Hospital (Montreal) to address the concerns increasingly being voiced by active middle-aged individuals at risk for AD. A positive family history of AD and/or perceived changes in personal cognitive function (predominantly short-term memory) are main reasons for referral. The primary objectives of ARAC are to (i) ascertain, inform and mitigate the risks of developing AD in cognitively-healthy persons aged 40-65 based on best available medical and epidemiological evidence, (ii) conduct scientific research on midlife dementia risk and prevention in this population and (iii) provide instruction in dementia risk assessment and management to health professionals, clinical/research fellows, medical residents and students. ARAC infrastructure, evaluation protocol, risk profile classification scheme, interventions, knowledge dissemination program, case vignettes, and seminal research projects are described. CONCLUSIONS: It is hoped that ARAC and similar initiatives will help prevent or delay dementia by innovating effective interventions based on increasingly nuanced estimation of modifiable AD risk in presymptomatic persons.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.094 | 0.019 |
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".