Rates of Cognitive Decline in 100 Patients With Alzheimer Disease
Bibliographic record
Abstract
Background: In the state of Louisiana, the prevalence of Alzheimer disease (AD) is projected to increase 26.4% by 2025 because of the rapidly increasing geriatric population. While significant research is available on risk factors for developing AD, less data are available regarding AD progression and the rate of change among patients with the disease. To date, no research has established the baseline cognitive decline of patients with AD residing in New Orleans, Louisiana. Methods: We evaluated 100 patients in the Ochsner Health system from September 2013 to December 2019 who had a diagnosis of AD and repeated Mini-Mental State Examination (MMSE) or Montreal Cognitive Assessment (MoCA) scores to determine annual rates of decline. Associated variables that were analyzed included race, age at diagnosis, social factors, and comorbidities. Results: The average annual rates of decline for MMSE and MoCA scores were 2.43 (SD 2.82) points and 2.39 (SD 1.88) points, respectively. Our results were significant for a faster rate of decline in MMSE scores among smokers (3.50 points, SD 3.20) vs nonsmokers (1.54 points, SD 2.07). We found no significant difference in MoCA scores for smokers vs nonsmokers, in addition to other demographic and lifestyle variables. Conclusion: The rate of decline seen in an urban population of patients with AD is lower than the average rate of decline reported in the literature, a finding that can help inform future interventional studies that use rate of decline as a primary outcome.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".