Quality of life in patients with subjective cognitive impairment compared with those diagnosed with mild cognitive impairment or dementia referred to a rural and remote memory clinic
Bibliographic record
Abstract
Abstract Background Perceived cognitive decline is a significant source of anxiety for many patients, especially as awareness of dementia grows. 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. Method 610 patients referred to our Rural and Remote Memory Clinic (RRMC) between 2004‐2019 were included in this study. As part of their RRMC assessment, each patient and their caregiver independently rated the patient’s QOL using the Quality of Life of the Patient (QOLPT) scale. We compared self‐reported and caregiver‐reported patient QOL scores in those with SCI (n = 166) to those diagnosed with MCI (n = 98), AD (n = 228), and non‐AD (n = 118). Result Patients with SCI self‐reported significantly lower QOL compared to patients with AD (mean 34.55 vs 35.96, p<0.05). Interestingly, the reverse was seen in caregivers: SCI caregivers rated patient QOL higher than AD caregivers (mean 34.61 vs 31.72, p<0.001). Patients with SCI also reported lower QOL than patients with MCI (mean 35.55 vs 36.97, p<0.05). SCI caregivers reported higher patient QOL than their non‐AD counterparts (mean 34.61 vs 31.17, p<0.001). Caregiver‐rated patient QOL was higher in those with MCI compared to AD (mean 34.52 vs 31.72, p<0.001). Patients with MCI self‐reported higher QOL scores compared to patients with non‐AD (mean 36.97 vs 34.81, p<0.05). Similarly, MCI caregivers reported higher patient QOL than non‐AD caregivers (mean 34.52 vs 31.72, p<0.001). 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, although patients with MCI self‐reported higher QOL than patients with non‐AD. Conversely, caregiver‐reported patient QOL was higher for patients with SCI than patients with AD and non‐AD. MCI caregivers also reported higher patient QOL compared to AD and non‐AD caregivers. This shows that SCI seriously impacts QOL. More work needs to be done 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".