Quality of Life in Patients with Subjective Cognitive Impairment Referred to a Rural and Remote Memory Clinic
Bibliographic record
Abstract
BACKGROUND: We sought to compare whether quality of life (QOL) in patients with subjective cognitive impairment (SCI) who performed normally on a neuropsychological battery significantly differed from those diagnosed with mild cognitive impairment (MCI), Alzheimer's disease (AD) or non-Alzheimer's dementia (non-AD) at initial assessment in a Rural and Remote Memory Clinic (RRMC). METHODS: = 118). RESULTS: Patients with SCI self-reported significantly lower QOL compared to patients with AD. Interestingly, the reverse was seen in caregivers: SCI caregivers rated patient QOL higher than AD caregivers. Patients with SCI also reported lower QOL than patients with MCI. SCI caregivers reported higher patient QOL than their non-AD counterparts. Caregiver-rated patient QOL was higher in those with MCI compared to AD. Patients with MCI self-reported higher QOL scores compared to patients with non-AD dementias. Similarly, MCI caregivers reported higher patient QOL than non-AD caregivers. No other comparisons were statistically significant. CONCLUSION: Although they lacked clinically significant cognitive deficits, patients with SCI self-reported significantly lower QOL than patients with MCI and AD. Conversely, caregiver-reported patient QOL was higher for patients with SCI than for patients with AD and non-AD. This shows that SCI seriously impacts QOL. More research is needed on how we can better support patients with SCI to improve their QOL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".