VentRa. Validation study of the ventricle feature estimation and classification tool to differentiate behavioral variant frontotemporal dementia from psychiatric disorders and other degenerative diseases
Bibliographic record
Abstract
ABSTRACT Introduction Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). Here we validate our previously developed automated tool using ventricular features (known as VentRa) for the classification of bvFTD vs a mixed cohort of neurodegenerative, vascular, and psychiatric disorders from a clinically representative independent dataset. Methods Lateral ventricles were segmented for 1110 subjects - 14 bvFTD, 30 other Frontotemporal Dementia (FTD), 70 Lewy Body Disease (LBD), 898 Alzheimer Disease (AD), 62 Vascular Brain Injury (VBI) and 36 Primary Psychiatric Disorder (PPD) from the publicly accessible National Alzheimer’s Coordinating Center dataset to assess the performance of VentRa. Results Using ventricular features to discriminate bvFTD subjects from PPD, VentRa achieved an accuracy of 84%, 71% sensitivity and 89% specificity. VentRa was able to identify bvFTD from a mixed age-matched cohort (i.e., Other FTD, LBD, AD, VBI and PPD) and to correctly classify other disorders as ‘not compatible with bvFTD’ with a specificity of 83%. The specificity against each of the other individual cohorts were 80% for other FTD, 83% for LBD, 83% for AD and 84% for VBI. Discussion VentRa is a robust and generalizable tool with potential usefulness for improving the diagnostic certainty of bvFTD, particularly for the differential diagnosis with PPD.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".