Attitudes of Neurologists Toward the Potential Role of Serum Neurofilament Light Chain Measurement in Treatment Decision-Making for People With Radiologically Isolated Syndrome
Bibliographic record
Abstract
Background: Serum neurofilament light chain (sNfL) levels reflect neuroaxonal damage and independently predict conversion to multiple sclerosis (MS) in individuals with radiologically isolated syndrome (RIS). This study aimed to assess how sNfL testing influences neurologists’ decisions regarding disease-modifying treatment (DMT) in the management of RIS. Methods: A noninterventional, web-based study was conducted among neurologists actively involved in MS care across Spain. Participants reviewed a simulated case of a 24-year-old woman diagnosed with RIS, characterized by a brain MRI showing 1 juxtacortical and 8 periventricular T2 hyperintense lesions, cerebrospinal fluid oligoclonal bands, and sNfL levels of 24 pg/mL. The neurologists had to decide whether to recommend initiating DMT or to schedule a reassessment in 6 to 12 months, with the latter considered a decision misaligned with emerging evidence (DMEE). Results: A total of 116 neurologists participated in the study (mean age, 41.9 years; 53.4% men). Overall, 58.6% (n = 68) opted against recommending the initiation of DMT. Lack of full dedication to MS care (OR, 2.52; 95% CI, 1.02-6.20; P = .045) and limited perception of sNfL benefits (OR, 1.03; 95% CI, 1.01-1.05; P < .001) were associated with DMEE. Conclusions: In a simulated high-risk RIS scenario with elevated sNfL levels, most neurologists refrained from recommending the initiation of DMT. These findings reinforce the need to increase awareness of prognostic factors for RIS-to-MS conversion and the utility of sNfL testing in guiding therapeutic decisions.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".