Higher serum β2-microglobulin is a predictive biomarker for cognitive impairment in spinal cord injury
Bibliographic record
Abstract
Objective Recent studies have suggested that high levels of β2-microglobulin are linked to cognitive deterioration; however, it is unclear how this connects to spinal cord injury (SCI). This study sought to determine whether there was any association between cognitive decline and serum β2-microglobulin levels in patients with SCI. Methods A total of 96 patients with SCI and 56 healthy volunteers were enrolled as study participants. At the time of enrollment, specific baseline data including age, gender, triglycerides (TG), low-density lipoprotein (LDL), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), smoking, and alcohol use were recorded. Each participant was assessed by a qualified physician using the Montreal cognitive assessment (MoCA) scale. Serum β2-microglobulin levels were measured using an enzyme-linked immunosorbent assay (ELISA) reagent for β2-microglobulin. Results A total of 152 participants were enrolled, with 56 in the control group and 96 in the SCI group. There were no significant baseline data differences between the two groups (p > 0.05). The control group had a MoCA score of 27.4 ± 1.1 and the SCI group had a score of 24.3 ± 1.5, with the difference being significant (p < 0.05). The serum ELISA results revealed that the levels of β2-microglobulin in the SCI group were considerably higher (p < 0.05) than those in the control group (2.08 ± 0.17 g/mL compared to 1.57 ± 0.11 g/mL). The serum β2-microglobulin level was used to categorize the patients with SCI into four groups. As serum β2-microglobulin levels increased, the MoCA score reduced (p < 0.05). After adjustment of baseline data, further regression analysis showed that serum β2-microglobulin level remained an independent risk factor for post-SCI cognitive impairment. Conclusions Patients with SCI had higher serum levels of β2-microglobulin, which may be a biomarker for cognitive decline following SCI.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".