O2–12–04: Scientific validity and ethics of freely accessible online tests for Alzheimer's disease
Bibliographic record
Abstract
North American demographics are rapidly shifting towards an increasing number of older adults, and the prevalence of Alzheimer's disease is projected to rise significantly. The active promotion of empowered, healthy aging is a priority. Communication of health information through online sources is widespread, easily accessible and sought by a large proportion of adults. Self-diagnosis behavior in particular is increasingly popular online, and freely accessible tests for Alzheimer's disease are available on the Internet. Little is known about the scientific validity and reliability of these tests, and ethics-related factors including research and commercial conflict of interest, confidentiality and consent. To address this knowledge gap, we used information-mining techniques to retrieve 16 online tests for Alzheimer's disease and used content analysis to establish the characteristics of each test. We then conducted an expert panel review of each of the tests using 5-point Likert Scales (1: very poor, 2: poor, 3: fair, 4: good, 5: excellent) to evaluate scientific validity and reliability, appropriateness of human-computer interaction features and ethics-related factors. We found that most online tests for AD (12/16) scored low (very poor or poor) for overall scientific validity and reliability. In terms of appropriateness of the human-computer interface for an older adult population, the majority of tests (10/16) scored fair across all criteria. The scores for ethics-associated factors were the lowest - over all criteria evaluated, a majority of tests scored very poor (9/16), and the remainder (7/16) scored poor. Overall, the scientific quality of freely accessible tests online is low and these tests conform poorly to conventional guidelines around consent, conflict of interest and other ethical considerations. These findings have significant implications for the growing computer-literate older adult population. The issues uncovered suggest that further evidence and informed policy are needed to promote the greatest benefits from tools and information available on the Internet.
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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 | 0.101 | 0.286 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".