Bibliographic record
Abstract
Frontotemporal lobar degeneration (FTLD) is the underlying pathological substrate of frontotemporal dementia (FTD) and related syndromes. These syndromes feature diverse clinical presentations-behavioral, cognitive, and motor deficits-and significant pathological heterogeneity, with most cases linked to tau or TAR DNA-binding protein 43 (TDP-43), and 5% related to fused in sarcoma (FUS). While most cases of FTLD are sporadic, around 10-20% of patients harbor an autosomal dominant mutation, typically in MAPT (tau), GRN (progranulin), or C9orf72. Detection of FTLD often occurs at advanced disease stages, limiting the opportunity for timely intervention. Early detection is critical for implementing disease-modifying therapies, yet there remains a pressing need for diagnostic biomarkers capable of identifying the specific underlying pathology, particularly tau and TDP-43, in the early stages of the disease. Fluid biomarkers have played a transformative role in advancing therapeutic strategies for Alzheimer's disease (AD), providing a roadmap for biomarker-driven research in FTLD. This session will explore the state-of-the-art developments in biomarkers for FTLD. Beyond CSF and plasma, innovative approaches such as skin biopsies are being investigated for detecting pathological aggregates of tau, TDP-43, or related molecular changes. These biomarkers hold potential for both detecting FTLD and differentiating between its major pathological subtypes: FTLD-tau and FTLD-TDP-43. Biomarkers such as neurofilament light chain (NfL) are robust but non-specific markers of neurodegeneration and so lacks specificity for FTLD or its pathological subtypes. Proteomic and metabolomic approaches have recently enabled the discovery of novel candidate biomarkers that could aid in more specific differentiation. Emerging data suggest that markers of neuroinflammation and synaptic dysfunction may offer greater specificity for FTLD subtypes. Biomarkers in FTLD are not only pivotal for diagnosis but also for tracking disease progression and monitoring response to emerging therapies. A comprehensive biomarker panel, incorporating markers of neurodegeneration, neuroinflammation, and specific pathology, may eventually enable personalized treatment approaches as well as evaluate the role of co-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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".