The Relationship Between Subjective Cognitive Decline and Objective Cognitive Performance in Older Adults With Treatment-Resistant Late-Life Depression: The Role of Depression Severity
Bibliographic record
Abstract
OBJECTIVES: Examine the role of depression severity in linking subjective and objective indicators of cognitive decline. METHODS: 354 participants (60+) were drawn from a multicenter longitudinal neuroimaging and neurocognitive study of TRLLD (the "OPTIMUM-NEURO" study). Subjective cognitive decline (SCD) was assessed using the everyday cognition scale. Objective cognitive performance (OCP) was assessed using the Repeatable Battery for Neuropsychological Status and subtests of the Delis-Kaplan Executive Functioning System. Depression severity was assessed using the clinician-administered Montgomery-Asberg Depression Rating Scale. Statistical analysis involved demographic-adjusted linear regression models and causal mediation analysis. RESULTS: Participant and study partner-reported SCD were associated with OCP in a broad range of cognitive domains. Greater depression severity was related to worse SCD and OCP but did not statistically mediate any SCD-OCP relationships. CONCLUSIONS: Among individuals with TRLLD, higher SCD is related to greater depression severity; however, SCD and depression severity each independently relate to OCP.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".