Staging Dementia From Symptom Profiles on a Care Partner Website
Bibliographic record
Abstract
BACKGROUND: The World Wide Web allows access to patient/care partner perspectives on the lived experience of dementia. We were interested in how symptoms that care partners target for tracking relate to dementia stage, and whether dementia could be staged using only these online profiles of targeted symptoms. OBJECTIVES: To use clinical data where the dementia stage is known to develop a model that classifies an individual's stage of dementia based on their symptom profile and to apply this model to classify dementia stages for subjects from a Web-based dataset. METHODS: An Artificial Neural Network (ANN) was used to identify the relationships between the dementia stages and individualized profiles of people with dementia obtained from the 60-item SymptomGuide (SG). The clinic-based training dataset (n=320), with known dementia stages, was used to create an ANN model for classifying stages in Web-based users (n=1930). RESULTS: The ANN model was trained in 66% of the 320 Memory Clinic patients, with the remaining 34% used to test its accuracy in classification. Training and testing staging distributions were not significantly different. In the 1930 Web-based profiles, 309 people (16%) were classified as having mild cognitive impairment, 36% as mild dementia, 29% as moderate, and 19% as severe. In both the clinical and Web-based symptom profiles, most symptoms became more common as the stage of dementia worsened (eg, mean 5.6 SD 5.9 symptoms in the MCI group versus 11.9 SD 11.3 in the severe). Overall, Web profiles recorded more symptoms (mean 7.1 SD 8.0) than did clinic ones (mean 5.5 SD 1.8). Even so, symptom profiles were relatively similar between the Web-based and clinical datasets. CONCLUSION: Symptoms targeted for online tracking by care partners of people with dementia can be used to stage dementia. Even so, caution is needed to assure the validity of data collected online as the current staging algorithm should be seen as an initial step.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".