Underlying structure of the MBI‐C in pre‐symptomatic and prodromic states of dementia: A multidimensional scaling approach
Bibliographic record
Abstract
Abstract Background The Mild Behavioral Impairment Checklist (MBI‐C) (Ismail et al., 2017) is a 34‐item scale that evaluates Neuropsychiatric Symptoms (NPS) in pre‐dementia states. Its underlying structure has been little studied and remains largely unknown. Factor analysis‐based approaches may have difficulty finding stable relationships between NPS collected on a checklist to account for the large heterogeneity of manifestations. Thus, our objective was to analyze the underlying structure of the MBI‐C using an alternative approach. Method Eighty‐two MCI and 117 SCD older adults of the CompAS were recruited in primary care health centers. Asymptomatic participants were excluded from the analyses. A weighted model of multidimensional scaling (MDS) will be used. A two‐step bidimensional weighted dichotomous MDS was performed. All items were included in the first step. Items closely associated with each dimension (1 SD above or below the mean) were selected in a second step to obtain the final model solution. Results The results obtained in the two analyzes were similar in terms of the good fit of the models, the type of two‐dimensional solution and the group weights. Model weights were also similar for the three diagnostic groups. The final model was built considering the 12 most relevant items selected in the first step analysis and showed optimal fit indices (stress‐II = .59; D.A.F. = .94). Figure 1 shows the coordinates for the 12 selected items in a bidimensional solution. Dimension I (right‐left) differentiate high and low emotional activation of NPS. Dimension II (top‐down) distinguishes between high and low behavioral activation. The combination of both generates four quadrants, indicating symptoms of resistance (Q‐I: low emotional and high behavioral activation), restlessness (Q‐II: high emotional and behavioral activation), flattening (Q‐III: low emotional and behavioral activation) and desolation (Q‐IV: high emotional and low behavioral activation) (see Figure 1). Conclusions The results suggest that two dimensions underlie the most discriminant NPS included in scale (i.e., emotional and behavioral activation). These two dimensions seem to differentiate between four NPS states (resistance, restlessness, flattening, and desolation), which could be the most useful NPS in the determination of risk factors for predementia patients.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".