Serum Neurofilament Light as a Neuropsychiatric Disorder Screening Test in Psychiatric Emergency Settings
Bibliographic record
Abstract
OBJECTIVE: The authors evaluated serum neurofilament light chain (sNfL) as a blood-based biomarker to distinguish primary psychiatric disorders from psychiatric presentations of neurological or general medical etiology (i.e., neuropsychiatric disorders) in psychiatric emergency departments (PEDs), where rapid diagnosis is essential and access to advanced tests is often limited. METHODS: Data were collected from 846 patients with psychiatric disorders (17% anxiety, 34% mood, 9% personality, 32% psychotic, and 7% substance use) and 20 patients with neuropsychiatric disorders (35% neurocognitive, 20% delirium, and 55% general medical causes). sNfL levels were measured with the SIMOA (Single Molecule Array) platform. Analysis of covariance and logistic regression were conducted to assess sNfL differences between psychiatric and neuropsychiatric patients. Receiver operating characteristic curve analysis was used to determine diagnostic accuracy, with Youden's index employed to identify optimal thresholds. RESULTS: =0.24). Logistic regression confirmed that sNfL levels strongly predicted the diagnostic group. The optimal cutoff for sNfL was 30.6 pg/mL, with a sensitivity of 0.90 and specificity of 0.94. Subgroup analyses suggested that age-specific thresholds could improve diagnostic accuracy. CONCLUSIONS: sNfL is a promising biomarker for rapid differentiation in PEDs between primary psychiatric disorders and psychiatric conditions of general medical or neurological origins, potentially improving diagnostic accuracy and speed. Future research is needed with more diverse, prospective cohorts with a wider range of diseases to replicate the clinical utility of sNfL.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".