Comorbidity and the rate of cognitive decline in patients with Alzheimer dementia
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the impact of comorbidity on cognitive and functional decline in patients with Alzheimer dementia (AD). METHODS: One hundred and two AD outpatients examined at the Psychiatry Department of the CF2 Polyclinic in Bucharest, Romania and re-evaluated after 2 years. Comorbidity was rated using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G). RESULTS: Baseline mean age (SD) was 75.4 (8.2) years, median CDR (range) was 2 (1-3), and mean MMSE (SD) 14.2 (4.9). MMSE declined to 11.2 (4.8) during follow-up. Baseline mean total CIRS-G score (SD) was 13.8 (5.4), median number of endorsed categories (range) was 8 (1-14), and mean severity index (SD) 1.9 (0.4). Main comorbidity areas were cardiovascular, ear, nose and throat, genitourinary, musculoskeletal/integument, and neurological. Severity of comorbidity increased with dementia severity (p <0.001). Baseline comorbidity was related to increased rate of cognitive decline; truncated regression coefficients (p-values) were 0.01 (0.02) for CIRS-G total score, and 0.15 (0.006) for severity index (controlled for age, sex, education, and AD treatment). Faster cognitive decline was associated with faster functional decline: OR (95% CI) was 5.2 (1.9-13.6) for increased rate of ADL change and 3.8 (1.0-14.1) for increased rate of IADL change (controlled for age, sex, education, AD medication, and comorbidity). Comorbidity tended to increase functional decline; however, the associations were not statistically significant. CONCLUSIONS: In this group of patients with AD, comorbidity increased the rate of cognitive decline. Considering comorbidity instead of focusing on separate conditions may be more helpful in managing AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".