P1‐053: Differentiating the frontal variant of Alzheimer's disease
Bibliographic record
Abstract
Individuals with a clinical diagnosis of Alzheimer's Disease (AD) may have prominent features of executive dysfunction and language impairment as well as behavioral abnormalities early in the disease (“high frontality”). When this occurs differentiation from Frontotemporal Dementia (FTD) is difficult. It is hypothesized that AD patients with high frontality may have clinical and pathological features that distinguish them from less frontal AD patients. In a well- characterized cohort of people with cognitive impairment, we used the Frontal Behaviour Inventory (FBI) in an attempt to identify AD patients with prominent frontal features (high-FBI AD) and distinguish them from the remainder of AD patients (low-FBI AD). The 18 high-FBI AD patients were compared with the 26 FTD patients who had an FBI performed and the 53 other low FBI AD patients. The individual FBI items did not differ significantly between the FTD and the high-FBI AD patients, and the high FBI AD patients were more like the FTD patients than the other AD patients with respect to presence of a family history of AD, proportion with homozygous apolipoprotein E4 status, disability as measured by the Disability Assessment for Dementia (DAD) Scale and the Functional Rating Scale (FRS) and neuropsychiatric impairment as measured by the Neuropsychiatric Inventory (NPI). Memory symptom duration was similar in the high FBI AD group compared to the low FBI AD group. There is a subgroup of AD patients with high frontality that can be clinically distinguished from the remainder of AD patients. This group may also have different pathology to the less frontal AD patients.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.002 |
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".