Neuropathological background of phenotypical variability in frontotemporal dementia
Bibliographic record
Abstract
Frontotemporal lobar degeneration (FTLD) is the umbrella term encompassing a heterogeneous group of pathological disorders. With recent discoveries, the FTLDs have been show to classify nicely into three main groups based on the major protein deposited in the brain: FTLD-tau, FTLD-TDP and FTLD-FUS. These pathological groups, and their specific pathologies, underlie a number of well-defined clinical syndromes, including three frontotemporal dementia (FTD) variants [behavioral variant frontotemporal dementia (bvFTD), progressive non-fluent aphasia, and semantic dementia (SD)], progressive supranuclear palsy syndrome (PSPS) and corticobasal syndrome (CBS). Understanding the neuropathological background of the phenotypic variability in FTD, PSPS and CBS requires large clinicopathological studies. We review current knowledge on the relationship between the FTLD pathologies and clinical syndromes, and pool data from a number of large clinicopathological studies that collectively provide data on 544 cases. Strong relationships were identified as follows: FTD with motor neuron disease and FTLD-TDP; SD and FTLD-TDP; PSPS and FTLD-tau; and CBS and FTLD-tau. However, the relationship between some of these clinical diagnoses and specific pathologies is not so clear cut. In addition, the clinical diagnosis of bvFTD does not have a strong relationship to any FTLD subtype or specific pathology and therefore remains a diagnostic challenge. Some evidence suggests improved clinicopathological association of bvFTD by further refining clinical characteristics. Unlike FTLD-tau and FTLD-TDP, FTLD-FUS has been less well characterized, with only 69 cases reported. However, there appears to be some associations between clinical phenotypes and FTLD-FUS pathologies. Clinical diagnosis is therefore promising in predicting molecular pathology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".