Biomarkers in Traumatic Spinal Cord Injury—Technical and Clinical Considerations: A Systematic Review
Bibliographic record
Abstract
Objective. To examine (1) if serological or cerebrospinal fluid (CSF) biomarkers can be used as diagnostic and/or prognostic tools in patients with spinal cord injury (SCI) and (2) if literature provides recommendations regarding timing and source of biomarker evaluation. Data Sources. A systematic literature search to identify studies reporting on diagnostic and prognostic blood and/or CSF biomarkers in SCI was conducted in PubMed/MEDLINE, CINAHL, Science Direct, The Cochrane Library, ISI Web of Science, and PEDro. Study Selection. Clinical trials, cohort, and pilot studies on patients with traumatic SCI investigating at least one blood or CSF biomarker were included. Following systematic screening, 19 articles were included in the final analysis. PRISMA guidelines were followed to conduct this review. Data Extraction. Independent extraction of articles was completed by 2 authors using predefined inclusion criteria and study quality indicators. Data Synthesis. Nineteen studies published between 2002 and April 2019 with 1596 patients were included in the systematic review. In 14 studies, blood biomarkers were measured, 4 studies investigated CSF biomarkers, and 1 study used both blood and CSF samples. Conclusions. Serum/CSF concentrations of several biomarkers (S100b, IL-6, GFAP, NSE, tau, TNF-α, IL-8, MCP-1, pNF-H, and IP-10) following SCI are highly time dependent and related to injury severity. Future studies need to validate these markers as true biomarkers and should control for secondary complications associated with SCI. A deeper understanding of secondary pathophysiological events after SCI and their effect on biomarker dynamics may improve their clinical significance as surrogate parameters in future clinical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".