Serum neurofilament light chain – A potential biomarker for polyneuropathy in type 2 diabetes?
Bibliographic record
Abstract
AIMS: To investigate the relationship between neurofilament light chain (NfL) and the presence and severity of diabetic polyneuropathy (DPN). METHODS: We performed cross-sectional analysis of data from 178 participants of the ADDITION-Denmark cohort of people with screen-detected type 2 diabetes and 32 healthy controls. Biobank serum samples were analyzed for NfL using single-molecule array. DPN was defined by Toronto criteria for confirmed DPN. Original and axonal nerve conduction study (NCS) sum z-scores were used as indicators of the severity of DPN and peripheral nerve damage. RESULTS: 39 (21.9%) participants had DPN. Serum NfL (s-NfL) was significantly higher in participants with DPN (18.8 ng/L [IQR 14.4; 27.9]) than in participants without DPN (15.4 ng/L [IQR 11.7; 20.1]). There were no unadjusted s-NfL differences between controls (17.6 ng/L [IQR 12.7; 19.8]) and participants with or without DPN. Higher original and axonal NCS sum z-scores were associated with 10% higher s-NfL (10.2 and 12.1% [95% CI's 4.0; 16.8 and 6.6; 17.9] per 1 SD). The AUC of s-NfL for DPN was 0.63 (95% CI 0.52; 0.73). CONCLUSIONS: S-NfL is unlikely to be a reliable biomarker for the presence of DPN. S-NfL is however associated tothe severity of the nerve damage underlying DPN.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".