Use of graphical markers for the differential diagnosis of primary progressive aphasia subtypes
Bibliographic record
Abstract
Abstract Background Primary Progressive Aphasia (PPA) brings together a group of neurodegenerative pathologies whose principal characteristic is to start with a progressive language disorder. Three main PPA subtypes were established, depending on the affected brain regions and the type of language disorder: the logopenic subtype (lvPPA), the non‐fluent/agrammatic subtype (nfavPPA) and the semantic subtype (svPPA) (Gorno‐Tempini et al., 2011). PPA diagnosis is often delayed in non‐specialised clinical settings. With the development of technologies, new diagnostic tools can be used, such as writing on a touch pad (Plonka et al., 2020). We have already highlighted differences between patients with typical Alzheimer's Disease (AD) and healthy controls (Gros et al., 2019), but the kinematic writing parameters are still understudied in the differential diagnosis of PPA subtypes. Method 29 subjects with Primary Progressive Aphasia (lvPPA N= 18; nfavPPA N=6; svPPA N=5) were included in this study. They performed ten graphical markers tasks (2 non‐cognitive and non‐linguistic tasks, 4 cognitive and non‐linguistic tasks and 4 linguistic tasks) on an I‐Pad® tablet. Different writing parameters were extracted: writing pressure, velocity, jerk and stroke. Result Preliminary results revealed a main effect of diagnosis in linguistic tasks on average velocity (p= 0,041) and on average stroke (p= 0,029). The two‐by‐two comparison of different variants of PPA also showed significative differences: 1) lvPPA vs nfavPPA: significant differences in the average velocity, jerk and stroke were found, with lvPPA participants showing a higher velocity (p= 0,037), jerk (p= 0,046) and stroke (p= 0,014) than nfavPPA participants in linguistic tasks. 2) lvPPA vs svPPA: significant difference in velocity was found in linguistic task (p=0,037). 3) nfavPPA vs svPPA: significant difference in maximum pressure was found in linguistic task (p= 0,044), with nfavPPA participants showing a lower maximum pressure than svPPA participants. Conclusion These preliminary results suggest that the use of graphical markers can be helpful in the differential diagnosis of PPA subtypes. A longitudinal study is ongoing to collect a bigger and more balanced PPA sample by including these markers within an already existing screening test battery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".