Scientific and ethical features of English‐language online tests for Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Freely accessible online tests for the diagnosis of Alzheimer's disease (AD) are widely available. The objective of this study was to evaluate these tests along three dimensions as follows: (1) scientific validity; (2) human-computer interaction (HCI) features; and (3) ethics features. METHODS: A sample of 16 online tests was identified through a keyword search. A rating grid for the tests was developed, and all tests were evaluated by two expert panels. RESULTS: Expert analysis revealed that (1) the validity of freely accessible online tests for AD is insufficient to provide useful diagnostic information; (2) HCI features of the tests are adequate for target users, and (3) the tests do not adhere to accepted ethical norms for medical interventions. DISCUSSION: The most urgent concerns raised center on the ethics of collecting and evaluating responses from users. Physicians and other professionals will benefit from a heightened awareness of these tools and their limitations today.
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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 | 0.063 | 0.256 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".