P2‐004: Network statistics to identify profiles of disease expression
Bibliographic record
Abstract
An exploratory analysis to establish a possible signal of dementia stage using network statistics. The data come from patients and carers who used an online symptom tracking tool, SymptomGuide (SG), at DementiaGuide.com. From these, 139 patients with between 8-25 symptoms each were classified either as mild (n=74) or moderate (n=65) dementia. Staging used an established artificial neural network with supervised learning. Using an established method, we created 100 symptom-based networks for each group. A network is composed of nodes (symptoms) and edges (significant associations between symptoms). For each network, a bootstrapped sample of 1000 samples with replacement was taken Cytoscape was used to derive network statistics. Average number of neighbors represents the average number of symptom associations. Clustering coefficient measures the degree of symptom clustering. Connected components quantifies number of unique clusters. Network heterogeneity reflects the tendency of symptom hubs. Network centralization measures the connectivity of the most connected symptom. Network density measures edge density. Network diameter measures the largest distance between two symptoms. Characteristic path length is the average distance between two connected symptoms. The shortest path length is the minimum characteristic path length found. Standard t-tests were used to test for differences in continuous data and Pearson's chi-squared for categorical data. Most of the sample (74.4%) were women. The mean age was 74.1 (95% CI 72.1-76.2) for mild; 77.9 (74.6-81.2) for moderate patients. Similar numbers of symptoms were seen in mild 9.8 (9.4-10.3) and moderate 10.1 (9.8-10.4). The mild group had significantly higher number of average neighbors 2.3 (2.2-2.3) vs 1.9 (1.8-1.9), network density 0.89 (0.087-0.091) vs 0.08 (0.07-0.08), network centralization 0.22 (0.21-0.24) vs 0.12 (0.12, 0.13) and network heterogeneity 0.73 (0.71-0.75) vs 0.55 (0.53-0.56) (t-test, p<0.01 for each). The moderate group showed significantly larger characteristic path lengths 2.98 (2.90-3.06) vs 2.54 (2.47-2.61) (t-test, p<0.01) and network diameter 6.75 (6.56-6.93) vs 5.01 (4.83-5.16) (t-test, p<0.01). In a group of mild versus moderate dementia patients with similar demographic characteristics, we found consistent differences in network properties. Overall, they suggest a pattern of increasing heterogeneity and loss of connectivity in the moderate versus the mild stage.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 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".