O3‐11‐02: LATENT CLASS ANALYSIS IDENTIFIES FUNCTIONAL DECLINE WITH AMSTERDAM IADL IN PRE‐CLINICAL ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Therapeutic trials in Alzheimer's disease (AD) start including participants at the earliest clinical stages to prevent the future onset of dementia. However, there is a lack of tools that are sensitive to subtle functional changes of early stage AD patients. This lack hinders the development of new therapies as it is difficult to prove their clinical relevance. We aimed to identify functional decline in preclinical AD using an innovative scale and statistical methodology. We analyzed 289 participants with subjective cognitive decline (SCD) from the INSIGHT-preAD cohort (Table 1; age range 70-85, follow-up duration of three years). PET-Amyloid and PET-FDG mean intensities in AD-related region of interest were used as marker of preclinical AD for amyloid deposition and hypometabolism respectively. To measure functional decline, we used the innovative questionnaire, Amsterdam IADL questionnaire (A-IADL-Q, Sikkes et al. 2012; Jutten et al. 2017). A latent class linear mixed model (LCLMM) was used to identify different groups of participants based on the evolution of their A-IADL-Q scores over 3 years follow-up. The secondary aim was to analyze differences between these subgroups and the relation to AD imaging markers. Five groups of A-IADL-Q evolution were identified by the LCLMM in the INSIGHT-preAD cohort (Figure 1B and 1C). 212 (73.4%) subjects were in a reference group with stable A-IADL-Q scores over 3 years. Another group with 23 (8.%) subjects had a persistent functional decline. This group had a higher amyloid load (p = 0.0005) and lower level of education (p = 0.0392) compared to the reference group (Figure 2). We did not find an association between glucose metabolism, age or sex and functional decline in this cohort.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.000 |
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".