P3–284: Visualizing complex patterns of dementia symptoms: A network approach
Bibliographic record
Abstract
Dementia trials face the considerable challenge of capturing the many ways in which clinically important changes occur. Network analysis, used in many other fields, offers the potential to allow ready recognition of change, even when individual effects are small. We employed a network visualization approach to display significant associations between 60 symptoms in relation to dementia severity. From two SymptomGuide ™ datasets (one clinic based, one online) data on 2269 individuals were aggregated for analysis. Mini-Mental State Examination (MMSE) scores were used for staging in clinical settings. A previously validated artificial neural network method was used for staging online. The presence or absence of 60 dementia symptoms was captured for each individual. Fisher's exact test was used between each pair of symptoms to test for significant co-occurrences, or significant anti-co-occurrence. P-values <0.01 were considered significant. Results were inputted into open source visualization software (Gephi) to represent symptom relationship patterns. Most (1408; 62%) patients were classified as either moderate or severe; 861 (38%) as showing either Mild Cognitive Impairment (MCI) or mild dementia. The MCI/Mild patients showed 30% greater connectivity than did the moderate/severe patients. Significant differences in symptomatic clustering were apparent by network visualization. Network edge thickness (representing significant co-occurrences showed variable clusters in relation to stage (e.g. verbal repetition more associated with executive dysfunction in mild dementia vs. impaired memory in severe dementia). Network visualization techniques appear to offer a new means of assessing complex relationships between the large numbers of symptoms that collectively characterize dementia. It also offers the potential to evaluate specific symptoms of interest. This might be of particular relevance to understanding symptoms such as impaired executive dysfunction, given significant inter-patient variability prior to dementia. It might also offer a novel means of comparing differences in symptoms between placebo and treatment groups in clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".