Potential diagnostic and prognostic value of serum and cerebrospinal fluid biomarkers in traumatic spinal cord injury: A systematic review
Bibliographic record
Abstract
It remains unclear whether biomarkers in the serum or CSF can be used for diagnosis or prognosis of spinal cord injuries (SCI). Therefore, a systematic review was undertaken to evaluate the prognostic or diagnostic value of serum and CSF biomarkers in assessing the severity of SCI and the outcome of patients. Two independent reviewers summarized the human studies retrieved from the electronic databases of Medline, Embase, Scopus and ISI Web of Science until April 2018. Seventeen studies were included (1065 patients aged 16-94 years old). Although the findings of the included studies suggest that inflammatory and structural proteins may be useful in assessing the severity of SCI and prediction of neurological outcome, the level of evidence is generally low. Given limitations to the available evidence, further investigation in this field is required using large prospective data sets with rigorous analysis of sensitivity, specificity and prediction.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".