Function-based dementia severity assessment for vascular cognitive impairment
Bibliographic record
Abstract
BACKGROUND/PURPOSES: Unimpaired activities of daily living (ADL) is essential for the diagnosis of normal cognition and mild cognitive impairment. However, diagnosis according to this concept is difficult to apply to patients comorbid with motor dysfunction. We aim to use a novel ADL questionnaire for operationally diagnosing unimpaired ADL in vascular cognitive impairment with no dementia (VCIND). METHODS AND PARTICIPANTS: This was a retrospective cohort study with both cross-sectional and long-term follow-up analysis. Patients with cerebrovascular disease with normal cognition (CVDNC), VCIND, and vascular dementia (VaD) were analyzed. Cutoff scores for differentiating different stages of cognitive impairment were compared between the new History-based Artificial Intelligent ADL questionnaire (HAI-ADL) and other tools. RESULTS: A total of 596 individuals were analyzed, including 40 CVDNC, 167 VCIND, 218 mild, 119 moderate, and 52 severe-dementia patients. The cutoff scores for determining unimpaired ADL in VCIND were 8.5, 3.5, 5, 100, and 60 in HAI-ADL, CDR-SB, IADL, BI, and CASI, respectively. HAI-ADL had the highest correlations with CDR-SB and the CDR staging system compared to other tools. Four models of progression rates from CVDNC/VCIND to VaD revealed it was much higher in the group with HAI-ADL > 8.5 compared to those with HAI-ADL≦8.5 with odds ratios of 3.75, 3.66, 3.31, and 2.77, respectively. CONCLUSION: Our study showed that HAI-ADL provides an operational determinates unimpaired ADL which is necessary for the diagnosis of VCIND. The predictive value for progression to dementia was proved by a long-term follow-up analysis of the research cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".