Visual Rating versus Volumetry to Detect Frontotemporal Dementia
Bibliographic record
Abstract
BACKGROUND/AIMS: Automated, volumetrically defined atrophy in the left anterior cingulate (LAC) and anterior temporal regions (LAT) on MRI can be used to distinguish most patients with frontotemporal dementia (FTD) from controls. FTD and Alzheimer's disease (AD) can differ in the degree of anterior temporal atrophy. We explored whether clinicians can visually detect this atrophy pattern and whether they can use it to classify the 2 groups of dementia patients with the same accuracy. METHODS: Four neurologists rated atrophy in the LAC and LAT regions on MRI slices from 21 FTD, 21 controls, and 14 AD participants. Inter-rater reliability and diagnostic accuracy were assessed. RESULTS: All 4 raters agreed on the presence of clinically significant atrophy, and their atrophy scoring correlated with the volumes, but without translation into high inter-rater diagnostic agreement. CONCLUSIONS: Volumetric analyses are difficult to translate into routine clinical practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".