Attitudes of Neurologists Toward Serum Neurofilament Light-Chain Testing in the Management of Relapsing–Remitting Multiple Sclerosis with Cognitive Impairment
Bibliographic record
Abstract
Background: Cognitive impairment has an impact upon the function and quality of life of patients with multiple sclerosis (MS). High-serum neurofilament light-chain (sNfL) levels predict disease progression and are also associated with impaired cognitive performance. This study aimed to assess the attitudes of neurologists toward sNfL testing as regards making therapeutic decisions in clinically and radiologically stable patients experiencing cognitive decline. Methods: A web-based observational study was conducted among neurologists caring for patients with MS. The role of sNfL in therapeutic decisions was assessed through a simulated case scenario describing a 31-year-old woman with relapsing–remitting MS for four years on glatiramer acetate. Her partner reported increased distractibility and difficulties in organizing daily activities over the past 18 months. There was no history of new relapses, and a follow-up brain MRI scan showed no new lesions. Her performance in the Symbol Digit Modalities Test decreased by 8 points from the previous year, with 46 correct answers. The patient had an sNfL level of 21 pg/mL, with no other identified factors that could have altered this value. The participants were tasked with deciding to either escalate treatment or to continue the current treatment and schedule the patient for reassessment in 6–12 months (defined as decisions misaligned with emerging evidence [DMEE]). Multivariate regression analysis was conducted to determine factors associated with DMEE. Results: One hundred and sixteen neurologists participated in the study. Almost 50% of the participants (n = 57) opted not to escalate treatment despite high sNfL levels. This was more common among neurologists not fully dedicated to MS care (60.5% vs. 43.6%). The multivariate analysis showed that being a neurologist not fully dedicated to MS (odds ratio [OR] = 2.35, 95% confidence interval [CI] 1.01–5.50; p = 0.04) and having a poor perception of sNfL benefits (OR = 1.02, 95% CI 1.00–1.04; p = 0.01) were associated with DMEE. Conclusions: Neurologists’ lack of full dedication to MS care and limited perception of sNfL’s clinical utility were key factors associated with suboptimal therapeutic decisions in a simulated case of cognitive decline with elevated sNfL. These findings underscore the need for increased education on the role of sNfL to improve evidence-based decision-making in MS management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".