S100B outperforms clinical decision rules for the identification of intracranial injury on head CT scan after mild traumatic brain injury
Bibliographic record
Abstract
Objective: To compare the classification accuracy of S100B to two clinical decision rules— Canadian CT Head Rule (CCHR) and New Orleans Criteria (NOC)—for predicting traumatic intracranial injuries (ICI) after mild traumatic brain injury (mild TBI).Methods: A secondary analysis of a prospective observational study of mild TBI patients was performed. The diagnostic performance of S100B for predicting ICI on head CT was compared to both the CHRR and NOC. Area under receiver operator characteristic (AUC) curves were used and multivariable analysis was used to create a new decision rule based on a combination of S100B and decision rule-related variables.Results: S100B had the highest negative predictive value (97.3%), positive predictive value (7.21%), specificity (33.6%) and positive likelihood ratio (1.3), and the lowest negative likelihood ratio (0.5). The proportion of mild TBI subjects with potentially avoidable head CT scans was highest using S100B (37.7%). The addition of S100B to both clinical decision rules significantly increased AUC. A novel decision rule adding S100B to three decision rule-related variables significantly improved prediction (p < 0.05).Conclusion: Serum S100B outperformed clinical decision rules for identifying mild TBI patients with ICI. Incorporating clinical variables with S100B maximized ICI prediction, but requires validation in an independent cohort.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".