Clinical use and reporting of neurofilament quantification in neurological disorders: A global overview
Bibliographic record
Abstract
INTRODUCTION: Neurofilament light chain (NfL) quantification aids in diagnosing and predicting neurological disorders, but clinical and laboratory practices vary across centers. Differences in result interpretation and reporting further challenge test commutability. This study aimed to review the global analytical and post-analytical methods used for NfL measurement in routine clinical practice across different contexts. METHODS: We established an international working group (WG) and distributed a survey to its members to gather information on context of use (COU), (pre) analytical methods, cutoff usage, as well as the interpretation and reporting of NfL measurements. RESULTS: Among the centers, 63% measured NfL in cerebrospinal fluid (CSF), 87% in blood, and 53% in both. COU was widespread, with 50% defining pathological cutoffs based on publications and 42% considering age. Reporting was primarily done through numeric results (95%). DISCUSSION: Harmonizing cutoffs, reporting, and interpretation across various clinical contexts will facilitate the incorporation of this biomarker into routine clinical practice. HIGHLIGHTS: Unique international overview of current analytical and post-analytical methods for neurofilament light chain (NfL) measurement in routine clinical practice. Tailored sheets for each neurological application. Strategies to harmonize cutoffs, reporting, and interpretation of NfL's measurement.
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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 | 0.081 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".