Overlap in Frontotemporal Atrophy Between Normal Aging and Patients With Frontotemporal Dementias
Bibliographic record
Abstract
Normal aging leads to frontocortical atrophy. The degree to which this complicates the use of frontotemporal atrophy as a diagnostic criterion for the frontotemporal dementias (FTDs) has not been reported. The present case-control study compared frontotemporal volumes delineated with semi-automatic brain region extraction [n=30 controls vs. 16 behavioral variant FTD (bvFTD) vs. 14 primary progressive aphasia]. Logistic regression identified those regions least helpful for distinguishing bvFTD and primary progressive aphasia from controls. Linear regression tested the correlation of duration of illness to atrophy severity. The control group showed high variance in volumes. Controls had right frontal lobe volumes that overlapped considerably with bvFTD volumes, but, as anticipated, the left anterior temporal volumes of interest showed 91% accuracy in distinguishing the aphasic subgroup from controls. Left-sided and not right-sided atrophy in the medial middle frontal region distinguished the bvFTD group from controls. The relegation of structural imaging to a supportive criterion for diagnosis is reasonable in the context of the range of atrophy due to normal aging. While volumetry identified left-sided atrophy as useful for identifying FTD cases, future studies should determine whether clinicians could make these distinctions on viewing routine diagnostic magnetic resonance imaging scans.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".