P4‐083: Connectivity Analysis of Dementia Symptoms Using Symptomguide<sup>tm</sup>
Bibliographic record
Abstract
SymptomGuide™ is a web based tool for persons with dementia and their caregivers. Since its launch in 2007, 701 users have built symptom profiles and provided diagnostic information. SymptomGuide™ data are analysed to discover knowledge about symptoms and their patterns, to facilitate the understanding of dementia symptoms for knowledge translation. This analysis presents the connectivity between symptoms. Of the 701 symptom profiles, 413 profilees are diagnosed with dementia (Dataset A), and 288 are not diagnosed with dementia (Dataset B). We compare the connectivity among symptoms between the two datasets. Connectivity among symptoms is defined by the level of independence of any two symptoms. We employ a bootstrapping method to repeatedly take part of samples (e.g., 51%) and calculate the level of independence between each symptom pair. If two symptoms show a high choice of being dependent, then there should be a connection between these two symptoms. The most connected symptoms of Dataset A are Household Chores, Misplacing Objects, Meal Preparation, Operating Gadgets/Appliances, Telephone Use and Physical Complaints. In comparison, the most connected symptoms of Dataset B are Physical Complaints, Shopping, Comprehension/ Understanding, Meal Preparation, Independence and Financial Management. Among those symptoms, two (Meal Preparation and Physical Complaints) appear in both Datasets as the most connected symptoms. We also divide the symptoms into five categories: Behaviour, Physical Manifestation, Daily Function, Executive Function, and Cognition. The most connected symptom category in Dataset A and B is Daily Function and Executive Function respectively. The least connected symptom category in both Datasets is Behaviour. The connectivity of individual symptom was different between people diagnosed with dementia, and people not yet diagnosed with dementia. Even so, the connectivity among symptom categories was consistent. Symptoms in people with memory complaints without a dementia diagnosis appear to difer in degree and not in kind from those with a dementia diagnosis. SymptomGuide™ connectivity graph for profiles being diagnosed with dementia (n = 413) (Th = 5; loops = 1000; sample = 51%) SymptomGuide™ connectivity graph for profiles not being diagnosed with dementia (n = 288) (Th = 5; loops = 1000; sample = 70%)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".