Application of NICE or SNC guidelines may reduce the need for computerized tomographies in patients with mild traumatic brain injury: a retrospective chart review and theoretical application of five guidelines
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injuries continue to be a significant cause of mortality and morbidity worldwide. Most traumatic brain injuries are classified as mild, with a low but not negligible risk of intracranial hemorrhage. To help physicians decide which patients might benefit from a computerized tomography (CT) of the head to rule out intracranial hemorrhage, several clinical decision rules have been developed and proven effective in reducing the amount of negative CTs, but they have not been compared against one another in the same cohort as to which one demonstrates the best performance. METHODS: This study involved a retrospective review of the medical records of patients seeking care between January 1 and December 31, 2017 at Helsingborg Hospital, Sweden after head trauma. The Canadian CT Head Rule (CCHR), the New Orleans Criteria (NOC), the National Emergency X-Radiography Utilization Study II (NEXUS II), the National Institute of Health and Care Excellence (NICE) guideline and the Scandinavian Neurotrauma Committee (SNC) guideline were analyzed. A theoretical model for each guideline was constructed and applied to the cohort to yield a theoretical CT-rate for each guideline. Performance parameters were calculated and compared. RESULTS: One thousand three hundred fifty-three patients were included; 825 (61%) CTs were performed, and 70 (5.2%) cases of intracranial hemorrhage were found. The CCHR and the NOC were applicable to a minority of the patients, while the NEXUS II, the NICE, and the SNC guidelines were applicable to the entire cohort. A theoretical application of the NICE and the SNC guidelines would have reduced the number of CT scans by 17 and 9% (P = < 0.0001), respectively, without missing patients with intracranial hemorrhages requiring neurosurgical intervention. CONCLUSION: A broad application of either NICE or the SNC guidelines could potentially reduce the number of CT scans in patients suffering from mTBI in a Scandinavian setting, while the other guidelines seemed to increase the CT frequency. The sensitivity for intracranial hemorrhage was lower than in previous studies for all guidelines, but no fatality or need for neurosurgical intervention was missed by any guideline when they were applicable.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".