Neuropsychological Testing in Pathologically Verified Alzheimer Disease and Frontotemporal Dementia
Bibliographic record
Abstract
BACKGROUND/AIMS: Differences in cognition between frontotemporal dementia (FTD) and Alzheimer disease (AD) are well described in clinical cohorts, but have rarely been confirmed in studies with pathologic verification. For emerging therapeutics to succeed, determining underlying pathology early in the disease course is increasingly important. Neuropsychological evaluation is an important component of the diagnostic workup for AD and FTD. Patients with FTD are thought to have greater deficits in language and executive function while patients with AD are more likely to have deficits in memory. OBJECTIVES: To determine if performance on initial cognitive testing can reliably distinguish between patients with frontotemporal lobar degeneration (FTLD) and AD neuropathology. In addition, are there other factors of the neuropsychological assessment that can be used to enhance the accuracy of underlying pathology? METHODS: Using a logistic regression we retrospectively compared neurocognitive performance on initial evaluation of 106 patients with pathologically verified FTLD (pvFTLD), with 558 pathologically verified AD (pvAD) patients from the National Alzheimer's Coordinating Center using data from the Uniform Data Set (UDS) and the neuropathology data set. RESULTS: As expected, pvFTLD patients were younger, demonstrated better memory performance, and had more neuropsychiatric symptoms than pvAD patients. Other results were less predictable: pvFTLD patients performed better on one test of executive function (trail making test part B) but worse on another (digit span backward). Performance on language testing did not strongly distinguish the 2 groups. To determine what factors led to a misdiagnosis of AD in patients with FTLD, we further analyzed a small group of pvFTLD patients. These patients demonstrated older age and lower Neuropsychiatric Inventory Questionnaire counts compared with accurately diagnosed cases. CONCLUSIONS: Other than memory, numerical scores of neurocognitive performance on the UDS are of limited value in differentiating FTLD from AD at the initial visit. These results highlight the difficulty of obtaining an accurate early diagnosis of FTLD and argue for adding supplemental tests to those included in the UDS to assess cognition in FTD and AD patients.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".