P3‐337: NETWORK VISUALIZATION TO DISCERN PATTERNS OF RELATIONSHIPS BETWEEN SYMPTOMS IN DEMENTIA
Bibliographic record
Abstract
The multidimensional characterization of complex diseases such as dementia usually demands a large number of cases in order to obtain reliable inferences. Even so, the number of participants in many studies, including clinical trials, is small in comparison to dementia complexity. Here we suggest an approach based on network visualization, combined with resampling, to discern the patterns of relationships among multiple dementia symptoms. The data came from on online symptom tracking tool on the SymptomGuide (SG) website,. Four hundred randomly selected patients were classified evenly into two groups, corresponding to mild and moderate-to- severe dementia, respectively. Of the 60 symptoms, the 25 symptoms with prevalence >10% were selected. For all pairs of symptoms relative risks (RR) of c-occurrence and their pointwise mutual information (PMI) were calculated. The null hypothesis (RR=1 or equivalently PMI=0) was assessed using bootstrap samples of 100 000 with replacement to calculate the RR and PMI between symptoms. The number of connections between mild and moderate/severe groups declined significantly (from 90± 5 to 56±3, p<0.001). The mild group showed 32 positive (synergetic) connections, compared to 16 in moderate/severe group. The number of negative connections (antagonistic) also decreased, from 58 in the mild group to 40 connections in moderate/severe group. For example, in mild dementia, decreased interest/initiative had 4 synergetic and 9 antagonistic relationships with other symptoms: these numbers declined to 3 and 5, respectively in moderate/severe dementia. For the symptom of insensitivity, the number of connections decreases dramatically from 12 in mild dementia to only 2 in moderate/ severe dementia. A network analytic approach, in combination with re-sampling, allowed us to discern patterns of relationships among dementia symptoms. Connectivity graphs allowed these relationships to be portrayed readily. The number of connections decreased as the severity of dementia increased. This novel approach has useful implications both for understanding of dementia-the patterns of symptoms characterising its stages-and for targeting important symptoms for treatment.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".