Serum neurofilament light as a biomarker of vulnerability to a second mild traumatic brain injury
Bibliographic record
Abstract
A second mild traumatic brain injury (mTBI) sustained prior to neuropathological recovery can lead to exacerbated effects. Without objective indicators of this neuropathology, individuals may return to activities at risk of mTBI when their brain is still vulnerable. With axonal injury recognized as a neuropathological hallmark of mTBI, we hypothesized that serum levels of neurofilament light (NfL), a highly sensitive biomarker of axonal injury, may be predictive of vulnerability to worse outcomes in the event of a second mTBI. Given this hypothesis is difficult to test clinically, we used a two-hit model of mTBI in rats and staggered inter-injury intervals by 1-, 3-, 7-, or 14-days. Repeat-mTBI rats were dichotomized into NfL high (NfL>median at the time of re-injury) and NfL low (NfL ex vivo diffusion tensor imaging. NfL levels at the time of the second mTBI were found to be predictive of vulnerability to re-injury, with NfL high rats displaying more neurological signs and a greater potentiation of NfL levels after the second mTBI. Importantly, this potentiation phenomenon remained even when limiting analyses to rats with longer inter-injury intervals, providing evidence that vulnerability to re-injury may not be exclusively dependent on inter-injury interval. Finally, NfL levels correlated with, and were predictive of, the severity of neurological signs following the second mTBI. These findings provide evidence that measurement of NfL during mTBI recovery may be reflective of the vulnerability to a second mTBI, and as such may have utility to assist return to sport, duty and work 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".