All‐cause mortality among veterans with mild cognitive impairment and Alzheimer’s dementia who have Intracerebral hemorrhage and subarachnoid hemorrhage
Bibliographic record
Abstract
Abstract Background Cerebral amyloid angiopathy (CAA) is a significant contributor to hemorrhagic stroke, notably lobar intracerebral hemorrhage (ICH) and convexity subarachnoid hemorrhage (SAH), both of which have been observed in patients with MCI/AD. To evaluate all‐cause mortality among veterans with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD) with/without Intracerebral hemorrhage and subarachnoid hemorrhage (ICH/SAH) in the United States (US) Veterans Affairs Healthcare System (VAHS). Method Veterans with MCI or AD were identified based on having clinical notes or diagnostic codes in the VAHS database (2010‐2019). ICH and SAH were identified with ICD‐10 codes I61.x and I60.x, respectively. Survival curves were generated, and Poisson regression was used to adjust for baseline characteristics. Result A total of 853,791 veterans with MCI/AD were included, with a mean age of 74 years, of which 96% were male, 5% Hispanic, 15% Black, and 74% White; 32% had MCI and 68% had AD. Approximately 0.6% of the overall sample had ICH/SAH. The observed mortality rates per 1000 person‐years were 130 for males and 60 for females and 138 for AD vs 102 for MCI. Mortality rates for Veterans with MCI/AD with and without ICH was 102 and 127, and with and without SAH was 83 and 127, respectively. Kaplan‐Meier curves showed a higher survival probability for Veterans with ICH/SAH events vs those without the events (P<0.01) and for AD vs MCI (P< 0.01; Figure). The mortality risk was lower for MCI than AD overall (IRR = 0.83, P< 0.01). The death rate in veterans with MCI/AD was statistically significantly lower for those with vs without ICH/SAH even after adjustment (IRR = 0.85, p<0.01). Death rate was higher in non‐Hispanic vs Hispanic (IRR = 1.23, P<0.01) and for White vs Black veterans (IRR = 1.05, P<0.01). Conclusion In US VAHS, AD was associated with an increased risk of death than MCI; ICH/SAH did not increase mortality risk. After adjustment, mortality rates were found to be 23% higher for non‐Hispanic vs Hispanic groups and 5% higher for White vs Black groups. Those findings will be further examined by incorporating Medicare data for Veterans who had dual eligibility for both VA and Medicare coverage.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".