The logopenic variant of primary progressive aphasia
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim is to explore the evolution of the logopenic variant of primary progressive aphasia as a distinct clinical entity and to outline recent advances that have clarified its clinical characteristics, neural underpinnings, and potential genetic and pathological bases. This is particularly relevant as researchers attempt to identify clinico-pathological relationships in subtypes of primary progressive aphasia in hopes of utilizing language phenotype as a marker of underlying disease. RECENT FINDINGS: Recent work has served to refine and expand upon the clinical phenotype of the logopenic variant. Logopenic patients show a unique pattern of spared and impaired language processes that reliably distinguish this syndrome from other variants of progressive aphasia. Specifically, they exhibit deficits in naming and repetition in the context of spared semantic, syntactic, and motor speech abilities. Further, there is a growing body of evidence indicating a possible link between the logopenic phenotype and specific pathological and genetic correlates. SUMMARY: Findings indicate that the logopenic variant is a distinct subtype of progressive aphasia that may hold value as a predictor of underlying pathology. Additional research, however, is warranted in order to further clarify the cognitive-linguistic profile and to confirm its relation to certain pathological and genetic processes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".