The diagnostic accuracy of the HITSNS prehospital triage rule for identifying patients with significant traumatic brain injury
Bibliographic record
Abstract
Diversion of suspected traumatic brain injury (TBI) patients to trauma centres may improve outcomes by expediting access to specialist neurosurgical care. This study aimed to determine the accuracy of the Head Injury Straight to Neurosurgery (HITSNS) triage rule for identifying patients with significant TBI. A diagnostic cohort study was performed using data from the HITSNS trial, the Trauma Audit and Research Network registry and the North East Ambulance service database. Sensitivity and specificity of the HITSNS triage rule were calculated against a reference standard of significant TBI, defined by a cranial Abbreviated Injury Scale score of at least 3 or by the performance of a neurosurgical procedure. A total of 3628 patients were included in the complete case analyses. The HITSNS triage tool demonstrated a sensitivity of 28.3% (95% confidence interval 21.8-35.4) and a specificity of 94.4% (95% confidence interval 93.6-95.2). The low sensitivity of the HITSNS triage rule suggests that a considerable proportion of patients with significant TBI may not be triaged directly to trauma centres, and further research is needed to improve the accuracy of bypass protocols.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".