A systematic review and meta‐analysis of dual‐task outcomes in subjective cognitive decline
Bibliographic record
Abstract
Abstract Subjective cognitive decline (SCD) may represent a preclinical manifestation of objective cognitive impairment. This review consolidated existing findings to determine if dual‐tasks objectively differentiate between individuals with SCD, motoric cognitive risk syndrome (MCR), mild cognitive impairment (MCI), and dementia. MEDLINE, Embase, PsycINFO, CENTRAL, AgeLine, and CINAHL were systematically searched for dual‐task studies examining older adults with SCD and analyzed using random‐effects meta‐analyses. Thirteen studies met the inclusion criteria. Within the SCD group, faster gait speed (SMD, 1.35; 95% CI, 0.57–2.13; p = .0007) and longer step length (SMD, 0.85; 95% CI, 0.44–1.26; p < .0001) favored the single compared to dual‐task condition. Faster gait speed was observed in the SCD group compared to MCI (SMD, 0.48; 95% CI, 0.28–0.67; p = .0001). A standardized dual‐task approach is needed to track gait parameters longitudinally, beginning with changes occurring at the SCD stage as these may precede future cognitive impairments. Highlights Evidence demonstrates that SCD may be a precursor to dementia. Faster dual‐task gait speed was observed in the SCD group compared to MCI. Slower dual‐task gait speed and shorter step length were observed within the SCD group. Dual‐tasks may help differentiate between preclinical and clinical cognitive decline. Dual‐tasks should be standardized and changes should be tracked longitudinally.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".