Clinical application and reporting of neurofilament quantification in neuropsychiatric disorders: an international overview
Bibliographic record
Abstract
BACKGROUND: The quantification of Neurofilament Light chain (NfL) in blood and cerebrospinal fluid (CSF) has proven valuable for diagnosing and prognosing various neurological disorders, including Alzheimer's disease, frontotemporal dementia, amyotrophic lateral sclerosis, parkinsonism, multiple sclerosis, and ischemic damage. However, there is considerable variability in clinical and laboratory practices between centers, primarily due to different contexts of use (COU), analytical methods, and the application of cutoffs or scales. Additionally, for the same biochemical profile, the interpretation and reporting of results may vary from one center to another, raising concerns about the commutability of the tests. To date, no consensus has been reached among laboratories to define the most appropriate use of cutoffs or conclusions/comments based on NfL profiles. METHOD: This international project involves 38 laboratories across 18 countries, all specialized in the biochemical diagnosis of neurological disorders. Using a questionnaire, we obtained descriptions from each center regarding their COU, pre-analytical and analytical protocols (including the biological fluid and methods used to quantify NfL). Through a consensus approach, we developed a harmonized strategy for the use and reporting of NfL results across different centres according to their respective COU. RESULTS: Among the centers, 63% quantified NfL in the CSF, 87% in blood and 53% in both fluids. COU were as follow: frontotemporal dementia (71%), Alzheimer disease (71%), multiple sclerosis (61%), amyotrophic lateral sclerosis (61%), psychiatric syndrome (45%), Creutzfeldt-Jakob disease (32%), Parkinson disease (39%), peripheral neuropathy (29%), traumatic brain injury (21%), Huntington's disease (13%), amyloid transthyretin related (11%) and cardiac arrest (8%). Most of the centers define pathological cutoffs based on publications (50%) and take age into account for this purpose (42%). Reporting is mostly transmitted through numeric results (95%). CONCLUSION: Harmonizing cutoffs, reporting, and interpretation across various clinical contexts will facilitate the incorporation of this biomarker into routine clinical practice.
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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 | 0.173 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".