Bibliographic record
Abstract
Self-reported memory complaints are common in elderly individuals. Depending on the population studied, and the manner in which the question was asked, between 20% and 56 % of elderly persons report problems with their memory. Recent population-based studies have shown that subjective memory loss (SML) predicts dementia or cognitive decline in seniors with normal cognition.1-5 How-ever, the usefulness of subjective memory complaints in identifying persons with cognitive impairment is not clear: some studies report a weak or no association between SML and cognition,6-9 while others report an association between SML and cognitive status.10,11 It is also unclear if the association between SML and cognition is due to potential confounding by depression12: those with depres-sive symptoms may be more likely to report SML and may have more impairment of cognition. SML is important for clinicians for 2 reasons: First, patients may present themselves with memory complaints. Clinicians then need to know if they should proceed with further cognitive assessment. Second, SML could be use-ful as a very simple screening test for cognitive impairment, if it accurately reflects true cognition. To clarify the association between cognitive status and SML, we conducted a secondary analysis of an exist-ing data set (the Manitoba Study of Health and Aging). Specifically, our objectives were the following: 1. to determine if subjective memory complaints are associated with Mini-Mental State Exam (MMSE) scores; 2. to determine if this association is independent of other factors such as age, sex, education, and depres-sive symptoms; and 3. to determine the sensitivity and specificity of subjec-tive memory complaints for the presence of cognitive impairment (dementia or cognitive impairment, no 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.458 | 0.227 |
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".