70)A型大動脈解離が疑われたが大動脈に病変を認めなかった両側頚動脈解離の1例(第204回日本循環器学会関東甲信越地方会)
Bibliographic record
Abstract
A family history of Alzheimer's disease (AD) increases one's risk of developing late-onset AD (LOAD), and a maternal family history of LOAD influences risk more than a paternal family history. Accumulating evidence suggests that a family history of dementia associates with AD-typical biomarker changes. We analyzed cross-sectional data from non-demented, mild cognitive impairment (MCI), and LOAD participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) with PET imaging using Pittsburgh Compound B (PiB, n = 99) and cerebrospinal fluid (CSF) analysis (n = 403) for amyloid-β peptide (Aβ) and total tau. We assessed the relationship of CSF and PiB biomarkers and family history of dementia, as well as parent gender effects. In the larger analysis of CSF biomarkers, we assessed diagnosis groups individually. In the overall sample, CSF Aβ, tau/Aβ ratio, and global PiB uptake were significantly different between family history positive and negative groups, with markers of increased AD burden associated with a positive maternal family history of dementia. Moreover, a maternal family history of dementia was associated with significantly greater PiB Aβ load in the brain in the parietal cortex, precuneus, and sensorimotor cortex. Individuals with MCI positive for a maternal family history of dementia had significantly more markers of AD pathophysiology than individuals with no family history of dementia. A family history of dementia is associated with AD-typical biomarker changes. These biomarker associations are most robust in individuals with a maternal family history, suggesting that a maternally inherited factor influences AD risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".