Can community‐based cognitive screening identify individuals with MCI associated changes in complex instrumental activities of daily living?
Bibliographic record
Abstract
Abstract Background Unrecognized deficits in complex instrumental activities of daily living, termed “preclinical disability”, have been identified in individuals experiencing mild cognitive impairment (MCI: Fieo & Stern, 2018; Peterson et al., 2017). Screening for difficulties in the performance of cognitively demanding complex IADLs may facilitate earlier identification of people on the threshold of MCI and can facilitate earlier intervention (Rodakowski et al., 2014). We examined the ability of three cognitive screening tests to identify individuals reporting IADL problems on the ADCS‐ADL‐MCI scale. Methods A sample of 87 adults living independently in the community were assessed with the Menu Task, Mini‐Cog, Montreal Cognitive Assessment (MoCA), the ADCS‐ADL, and the 18 and 24 item versions of the ADCS‐MCI‐ADL scale. Established cut‐off scores for each cognitive screen identified impaired and unimpaired groups. Mann‐Whitney U tests compared differences between unimpaired and impaired groups for each cognitive screen on the 18 and 24 item ADCS‐MCI‐ADL composite scores. Between group differences were also examined for each of the ADCS‐MCI‐ADL‐24 individual items. Results Sample demographics are presented in Table 1. Individuals impaired on the Menu Task had significantly lower composite scores on both the ADCS‐MCI‐ADL‐18 and the ADCS‐MCI‐ADL‐24 (p < .01), although no significant composite score differences were found for the Mini‐Cog (p = .061, p = .155) or the MoCA (p = .053, p = .099). Individual ADCS MCI items were also analyzed to explore the types of IADL impairments identified by each of the three cognitive screens scales. Different patterns of IADL impairment emerged for each of the cognitive screening measures. Individuals impaired on the Mini‐Cog had significantly lower ADCS‐ADL composite scores (p < .05). Conclusion Individuals living in the community who score as impaired on the Menu Task demonstrated significantly lower ADCS‐MCI‐ADL‐18 and ADCS‐MCI‐ADL‐24 composite scores, but no significant difference on ADCS‐ADL. Impairment on each of the cognitive screening measures was associated with lower scores on individual ADCS‐ADL‐MCI complex IADL items. Cognitive screening measures that detect early and subtle changes in function may lead to earlier diagnosis and interventions for cognitively complex IADL deficits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".