O2‐13‐06: Symptom profiles in relation to dementia staging
Bibliographic record
Abstract
Most novel treatments for Alzheimer's disease (AD) are likely to be disease-modifying without being curative. In consequence, understanding whether modified disease expression is clinically meaningful is essential. Understanding characteristic profiles can help guide decisions about clinical meaningfulness. Our goal was to characterize profiles of clinically meaningful symptoms in relation to dementia stage. Patients attending a tertiary care Memory Clinic and their care partners were asked to target 4-6 symptoms for treatment. Clinical diagnoses were made using standardized criteria and staging using the Global Deterioration Scale (GDS). Symptoms were defined using the 60-item SymptomGuideTM and grouped by domain (cognition, executive functions, daily function, and behaviour). Patterns of connections between symptoms were assessed using connectivity graphs based on point-wise mutual information, with confidence intervals estimated by bootstrapping. Of 385 patients, 105 met criteria for Mild Cognitive Impairment (MCI) and 269 for AD, of whom 237 were moderate and 32 were severe. Co-occurrence of symptom domains were found to show distinctive trends in symptom profile compositions. Progressing from MCI (GDS = 4) to Moderate (GDS = 5) to Severe (GDS = 6-7), it can been seen that symptomatic profile composition tends to consist of an increased concentration of Daily Function, Cognitive and Executive dysfunctions symptoms (chi-square, p<0.001). Neither Physical Manifestation nor Behavioural symptoms were found to significantly change in frequency as dementia severity increased (chi-square, P = 0.65). While disease-modifying treatments could simply slow typical dementia expression in AD, they also might alter patterns of disease expression. These data suggest that altering the impact on executive and daily function, and on cognition in relation to these domains is most likely to be both clinically meaningful and easy to detect.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".