ASSOCIATION BETWEEN MOTOR ACTIVITY AND TOTAL NEUROPSYCHIATRIC INVENTORY NURSING HOME VERSION SCORES
Bibliographic record
Abstract
Abstract Accurate neuropsychiatric symptoms (NPS) assessment monitoring is crucial for person-centered dementia care management. Doing so, however, is challenging, since current assessment tools rely on clinical observations, are time consuming, and are somewhat subjective in nature. The Neuropsychiatric Inventory Nursing Home Version (NPI-NH) is an informant-based assessment of 10 sub-domains of behavioral functioning where total NPI-NH score represents an overall behavioral disturbance level. We investigated the evidence pertaining to the diagnostic test accuracy (DTA) of motor activity tracking obtained via wearable sensor technology (WST) using total NPI-NH score as the gold standard in persons living with dementia (PLWD). This was part of a larger systematic review assessing the use of WST for NPS detection and monitoring carried out from inception until September 2022 (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=219917). A systematic literature search carried out in 7 library databases produced 12,928 articles from which 84 titles were retained for analysis. In total, 8 articles examined the validity of WST for assessing and monitoring of overall behavioral disturbance in PLWD, among which 5 studies used motor activity trackers. Dementia participants predominantly had Alzheimer’s, vascular or mixed dementia (40%, 20%, and 40% respectively), with mild-moderate severity. Three studies reported correlations between motor activity and total NPI score that ranged from 0.35 to 0.38. A random effects model indicated that the pooled correlation across studies was 0.37 (0.22-0.51), with no heterogeneity (I2=0%). While our sample reveals WST test accuracy as being consistently moderate, more research is necessary for confirmation.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| 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".