Clinical Accuracy of Serum Neurofilament Light to Differentiate Frontotemporal Dementia from Primary Psychiatric Disorders is Age-Dependent
Bibliographic record
Abstract
BACKGROUND: Symptoms of behavioral variant frontotemporal dementia (bvFTD) overlap with primary psychiatric disorders (PPD) making diagnosis challenging. Serum neurofilament light (sNfL) is a candidate biomarker to distinguish bvFTD from PPD, but large-scale studies in PPD are lacking. OBJECTIVE: Determine factors that influence sNfL from a large database of PPD patients, and test its diagnostic accuracy. DESIGN, SETTINGS, SUBJECTS, MEASUREMENTS: Clinical data of people aged 40-81 were obtained from healthy subjects (n = 69), and patients with PPD (n = 848) or bvFTD (n = 82). sNfL was measured using Simoa technology on an HD-X instrument. Data were analyzed using general linear models, and Receiver Operating Characteristic (ROC) curve analyses to determine global and age-specific sNfL cutoffs to distinguish bvFTD from PPD, using the Youden Index. RESULTS: sNfL increased with age, while sex, BMI and diabetes status were modestly associated with sNfL. sNfL was slightly higher in PPD than healthy subjects (14.1 versus 11.7 pg/mL), when controlling for covariates. sNfL was markedly lower in PPD than bvFTD (14.1 versus 44.1 pg/mL). sNfL could differentiate PPD from bvFTD with an AUC = 0.868, but the effect was driven by the younger subjects between age 40-60 years at a cutoff of 16.0 pg/mL. No valid cutoff was detected over age 60, however, values of sNfL above 38.5 pg/mL, or below 13.9 pg/mL, provided 90% diagnostic certainty of bvFTD or PPD, respectively. CONCLUSION: PPD have mildly elevated sNfL compared to healthy subjects but much lower than bvFTD. Results support the use of sNfL as a biomarker to differentiate PPD from bvFTD at age 60 or below, but accuracy decreases in older ages.
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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 | 0.004 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".