O3‐01‐03: Caring for Patients with Young Onset Dementia: What Factors Need Close Attention?
Bibliographic record
Abstract
Caring for persons with dementia often contribute to high levels of caregiver burden (CB). Demographic, cognitive and clinical characteristics and age of onset may have an impact on CB. Thus, it is important to recognize clinical variables at baseline that will aid in predicting future levels of CB. 150 patients were studied, 54 were classified as young onset dementia (YOD) and 96 as late onset dementia (LOD). Zarit Burden Inventory (ZBI) was used to measure the Level of CB and Neuro-Psychiatry Inventory – Questionnaire (NPI-Q) was used to measure caregivers’ distress level in response to participants’ neuro-psychiatry presentation. Cognitive function was measured using Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Characteristics of caregivers with high and low ZBI were analysed. The mean age of patients with YOD and LOD were 61.9 years and 77.2 years respectively. The mean MMSE scores (17.32 vs. 19.08; p=0.236) and MOCA scores (16.65 vs. 16.99; p=0.995) were not statistically different between the two groups. A logistic regression was performed to ascertain the factors that are associated with a higher caregiver burden. Family history of dementia (22.2%), presence of BPSD (21.2%), severity of disease, MMSE, ADCS-ADL and NPI-Q scores were factors associated with the likelihood that caregivers would report a high level of caregiver burden. The model explained 50% of the variance in the high Zarit scores and correctly classified 83% of cases. In particular, caregivers for individuals with YOD were almost 3 times more likely to experience high levels of caregiver burden. Intervention programs with strategies that provide comprehensive support for caregivers of YOD should be a priority in the holistic management of dementia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".