Visual rating scales of atrophy to differentiate Frontotemporal dementia from Primary psychiatric disorder: results from DIPPA study
Bibliographic record
Abstract
Abstract Background Adult‐onset behavioral changes and altered executive functioning are frequently caused by behavioural variant of Frontotemporal dementia (bvFTD) or primary psychiatric disorders (PPD) which overlap in terms of clinical presentations but differ in terms of treatment and prognosis. The multi‐centre study DIPPA‐FTD aims to develop diagnostic and prognostic algorithms to help distinguish sporadic bvFTD from late‐onset PPD. Although atrophy on neuroimaging seems to be an appropriate discriminator between neurodegenerative and non‐neurodegenerative conditions, the sensitivity of frontotemporal atrophy for bvFTD is relatively low, whereas decreased brain volumes may be found in conditions like schizophrenia. The aim of the study is to identify a discriminative pattern of brain atrophy between bvFTD and PPD in a clinically applicable way. Method The DIPPA FTD consortium retrospectively collected patients with late onset behavioral disturbances after the age of 45 years, across 5 centers (Milan, Amsterdam, Munich, Sydney, Montreal). The subjects had been classified either as bvFTD or PPD according to current clinical criteria and the majority had clinical follow‐up. Among the patients collected in the project, only those with T1 MRI available were selected. A protocol of 9 visual rating scales of atrophy (orbitofrontal, anterior cingulate, anterior temporal, fronto‐insula, medial temporal, parietal, medial ventricular and axial ventricular) was applied by a rater blind for clinical and demographic infomation. The rater was asked to classify the subjects as bvFTD or PPD. A composite score of frontal, temporal and ventricular rating scales was also calculated. Result The MRIs of 323 subjects (211 bvFTD and 112 PPD) were analysed. Groupwise bvFTD cases showed significantly higher scores of atrophy for all the scales used. The rater accurately predicted 72% of the cases (Sens 0.70, Spec 0.76) while the composite score reached an accuracy of 76% (Sens 0.77 Spec 0.74). ROC curve analysis showed that fronto‐insula was the single most useful scale in the differentiation between bvFTD and PPD (AUC 0.80). Conclusion Brain atrophy has a significant role in the discrimination between bvFTD and PPD. The use of visual rating scales, and in particular the fronto‐insula one, could increase the diagnostic accuracy in the clinical setting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".