Validation and generation of age-specific reference intervals for a new blood neurofilament light chain assay
Bibliographic record
Abstract
BACKGROUND: Neurofilament light chain (NfL) is the first blood biomarker for multiple sclerosis (MS) with utility for prognosis and disease monitoring. The Simoa NF-light is a research-based assay and is the most highly cited NfL method available, however development of assays specifically designed for routine clinical laboratories are necessary for clinical adoption. The Roche Elecsys NfL assay was developed specifically for routine laboratory use. METHODS: Specimens from 718 MS patients, paired serum-plasma and plasma-CSF specimens were used to compare the Simoa and Elecsys assays. Elecsys NfL precision, linearity, parallelism, limit-of-quantitation and interferences were assessed. RESULTS: Elecsys precision ranged from 1.8 % to 7.3 % coefficient of variation. Serum samples showed a high correlation (Pearson's r = 0.991) with a mean bias of -85.1 % compared to Simoa. Plasma-CSF correlations were high (Pearson's r = 0.993), with CSF values 54.8-fold higher than plasma. Age-specific 97.5th percentile reference limits were generated by transforming Elecsys to Simoa NfL values. The previously developed Simoa NfL reference app was extended to the Elecsys platform using the transformed Simoa reference values to provide age and BMI-adjusted Z scores and percentiles. CONCLUSIONS: The Elecsys NfL assay demonstrated precision and accuracy acceptable for use in clinical laboratories. The Elecsys assay generates NfL concentrations numerically lower than Simoa, however the results are highly correlated, requiring the need for Elecsys-specific reference values.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".