The generalizability of proposed reference values of plasma neurofilament light chain level in Thai healthy population
Bibliographic record
Abstract
Abstract Background Aging is an established confounding factor influencing the plasma levels of neurofilament light chain (NfL). While age‐specific cutoff values for NfL in healthy Caucasian populations have been documented, the potential variations in ethnically and socioeconomically underrepresented populations remain underexplored. This study aims to evaluate the acceptability of proposed NfL cutoff values in the healthy Thai population. Method The study included 233 healthy participants aged 18 years and above, drawn from the Comprehensive Geriatric Clinic at King Chulalongkorn Memorial Hospital, the Cognitive Aging Cohort, and controlled participants from various studies in Bangkok, Thailand. Plasma NfL levels were quantified using the single molecule array (Simoa®) NF‐light™ Advance kit. Utilizing reference threshold from previous studies (Simrén et al., 2022 and Bornhorst et al., 2022), the proportion of participants with NfL levels exceeding the proposed cutoff values were compared with the expected values. Age‐specific cutoffs were also determined using a methodology similar to previous studies. Result Among the participants (72.5% females, median age 65 (IQR: 56.0‐69.0), median Montreal Cognitive Assessment 27 (IQR: 25.0‐28.0), median Mini‐Mental State Examination 29.0 (IQR: 28.0‐30.0) and median education level 16.0 years (IQR: 16.0‐18.0)), plasma NfL levels exceeded the 95th percentile reference from Simrén et al. in 12% (95% CI: 8.1‐16.9%) and Bornhorst et al. in 9% (95% CI: 5.7‐13.4%). These proportions, along with their confidence intervals, surpassed the expected values of 5%. Quantile regression was employed to provisionally visualize the age‐specific NfL threshold among the Thai population (Figure 1). Conclusion Preliminary findings suggest that age‐specific cutoff values established from healthy Caucasian populations may not be universally applicable in Thailand. Factors such as ethnicity and social determinants of health may introduce confounding factors to the blood levels or pose negative effects on brain ageing. Given the increasing significance of NfL as a biomarker for various neurological diseases, there is a critical need to establish reference values for plasma NfL in diverse settings, particularly in lower‐income countries.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".