Quality of Life in People With Subjective Cognitive Decline
Bibliographic record
Abstract
Objective: Quality of life is extensively studied in older persons, but there are few studies that investigate it in people with subjective cognitive decline. Our aim was to evaluate the quality of life in a Romanian sample of individuals with subjective cognitive decline compared to controls while accounting for different possible moderators. To our knowledge, this is the first study to evaluate the quality of life in a Romanian subjective cognitive decline sample. Methods: We conducted an observational study to evaluate differences in the quality of life between subjective cognitive decline and controls. Participants were evaluated for subjective cognitive decline according to Jessen et al. We collected sociodemographic and clinical characteristics and information about physical activity. Quality of life was evaluated using the Short Form-36 questionnaire. Results: = .018) compared to the control group. Conclusion: Persons with subjective cognitive decline reported diminished quality of life compared to controls and differences were not explained by other sociodemographic and clinical characteristics evaluated. This area could prove to be an important target for nonpharmacological interventions in the subjective cognitive decline group.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".