Development of a telemedicine protocol for the diagnosis of Alzheimer's disease
Bibliographic record
Abstract
We developed a telemedicine protocol for diagnosis of Alzheimer's Disease (AD). Assessments by video-conferencing (remote) were compared with face to face (direct) assessments. Eight physicians performed direct assessments and two physicians conducted remote assessments. There was alternate allocation of direct or remote initial assessment. The participants were 20 subjects over 65 years living in a rural area and referred by general practitioners (GPs) because of cognitive impairment. Each assessment included a Standardised Mini Mental State Examination, Geriatric Depression Scale, Katz assessment of Activities of Daily Living, Instrumental ADL assessment, and the Informant Questionnaire for Cognitive Decline in the Elderly. Laboratory results and radiological imaging were available from referring GPs. There was good agreement for diagnosing Alzheimer's disease between telemedicine and direct assessment, kappa = 0.8 (P<0.0001). However, because of the small sample size, the presence of systematic bias could not be completely excluded. We conclude that it is possible to diagnose AD at a distance using telemedicine, but this requires validation with a larger 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 | 0.069 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".