P.002 Quality of life across types of dementia in rural and remote memory clinic patients
Bibliographic record
Abstract
Background: Quality of life (QOL) is of great importance in dementia. We examined QOL across types of dementia in patients presenting to a rural and remote memory clinic (RRMC). Methods: This analysis included 343 RRMC patients seen between 2004 and 2016. Patients were diagnosed with mild cognitive impairment (MCI, n=74), frontotemporal dementia (FTD, n=42), Alzheimer’s disease (AD, n=187), vascular dementia (VD, n=22), or Lewy Body dementia (DLB, n=18). Patients and caregivers completed questionnaires at their initial visit. Data collection included patient-rated patient QOL (QOL-PT), caregiver-rated patient QOL (QOL-CG), MMSE score, age, and other patient demographics. Statistical analysis assessed patient variables and differences in QOL across types of dementia using one-way ANOVA, χ2 tests, and t-tests. Results: QOL-PT did not differ by diagnosis, whereas QOL-CG did. QOL-CG was significantly higher in MCI (34.6±7.1) compared to FTD (30.9±5.2) and AD (31.7±5.9). QOL-PT and QOL-CG differed in certain dementia types. QOL-PT was significantly higher than QOL-CG in MCI (QOL-PT=37.3±5.0, QOL-CG=35.3±7.3), FTD (QOL-PT=37.2±6.1, QOL-CG=31.7±5.5), and AD (QOL-PT=37.0±9.7, QOL-CG=32.1±5.9). Conclusions: We found that QOL-PT does not differ across dementia types, QOL-CG is higher in MCI compared to FTD and AD, and patients rate their own QOL higher than their caregivers do in MCI, FTD, and AD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".