Accuracy of blood-based neurofilament light to different genetic frontotemporal dementia from primary psychiatric disorders
Bibliographic record
Abstract
BackgroundGenetic frontotemporal dementia (FTD) along with Alzheimer's disease (AD), is one of the most prevalent early-onset dementias. The differential diagnosis of FTD from primary psychiatric disorder (PPD) has been challenging due to significant symptom overlap, particular as FTD often presents with prolonged psychiatric prodromes.ObjectiveThis study aims to evaluate whether blood-based neurofilament light chain (NfL) can differentiate genetic FTD from PPD, and to determine a global clinical cutoff to differentiate genetic FTD carriers from PPD with high specificity and sensitivity.MethodsData (ages 40-81) were obtained from FTD mutation carriers (GENFI; n = 474; n = 120 C9orf72, n = 114 GRN, n = 50 MAPT, n = 190 controls), and PPD (Biobanque Signature; n = 848). Blood-based NfL was measured with SIMOA HD-X (BbS) and SIMOA HD-1 (GENFI).ResultsBlood-based NfL was higher in all symptomatic mutations compared to PPD. Mildly symptomatic (0 < FTLD CDR-SOB-NM < 4) C9orf72 and GRN carriers also had higher NfL. ROC curve revealed an optimal blood-based NfL cutoff of 22.1 pg/mL (J = 0.647) to distinguish symptomatic genetic FTD from PPD (78.5% sensitivity, 86.2% specificity, AUC = 0.908). For mildly symptomatic subjects, a cutoff of 16.2 pg/mL (J = 0.601) differentiated groups with 86.7% sensitivity and 73.5% specificity (AUC = 0.870).ConclusionsNfL holds potential as a blood-based biomarker for symptomatic genetic FTD carriers, with moderate accuracy to distinguish PPD from mild forms including C9orf72.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".