Serum Biomarkers as Adjuncts to the National Institute for Health and Care Excellence Head Injury Guidelines (NG232, 2023) When Selecting Patients with Traumatic Brain Injury for Computed Tomography: A Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury Study
Bibliographic record
Abstract
This article explores the diagnostic performance of a panel of six biomarkers (glial fibrillary acidic protein [GFAP], neurofilament light [NFL], neuron-specific enolase [NSE], S100 calcium-binding protein B [S100B], total tau [t-tau], and ubiquitin C-terminal hydrolase L1 [UCH-L1]) in the context of the “2023 UK National Institute for Health and Care Excellence (NICE) Head Injury: Assessment and early management (NG232)” guideline. Emphasis is placed on subjects where clinical equipoise remains concerning the decision for head computed tomography (CT), medium-risk subjects. All adult subjects from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) dataset with a complete biomarker profile and interpretable CT scan within 24 h of injury were classified as high, medium, and low-risk according to the NICE NG232 Clinical Decision Rule (CDR) for CT head imaging following head injury. In subjects classified as medium-risk, the area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of biomarkers to identify those with (1) CT abnormality or (2) potential neurosurgical lesion, with CT considered the gold standard diagnosis. A time-to-biomarker sub-analysis was performed in subjects with a time from injury to sampling within 6 h, in keeping with current clinical usage of biomarkers. Among 1979 CENTER-TBI participants with sufficient clinical information to facilitate classification, 385 subjects were classified as medium-risk. Biomarker concentrations were significantly higher in those with traumatic CT abnormalities as compared with those without for all biomarkers aside from NSE (all p < 0.05). When sampled within 24 h of injury, GFAP demonstrated the best diagnostic performance for CT abnormality (AUC 0.81 [0.77–0.86]), with NFL, t-tau, and UCH-L1 showing moderate performance. At a threshold to provide a 95% sensitivity, GFAP, NFL, t-tau, and UCH-L1 demonstrated specificities ranging from 18% to 33% corresponding to a potential reduction of total CT images performed in these subjects by 14–23%. S100B and UCH-L1 showed improved performance when biomarker sampling time was limited to 6 h following injury. In intoxicated subjects with a persistent Glasgow Coma Score of 13–14, biomarker levels were significantly higher in subjects with CT abnormality as compared with those without. In conclusion, serum biomarkers demonstrate potential for the reduction in CT scan requirements in those classified as medium-risk in reference to the NG232 CDR criteria. These results highlight a need for further prospective studies on the use of diagnostic TBI biomarkers in current emergency medicine practice, with future consideration given to the integration of biomarkers in the NICE NG232 head injury guidelines.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".