Criteria for Performing Cranial Computed Tomography for Chinese Patients With Mild Traumatic Brain Injury
Bibliographic record
Abstract
Computed tomography (CT) provides the primary diagnostic evidence for traumatic brain injury (TBI), but few positive traumatic findings are discovered in patients with mild TBI. In China, there are no existing criteria for selecting patients with mild TBI to undergo CT, and almost all of these patients undergo cranial CT in the emergency department. This retrospective study was performed to evaluate the necessity of cranial CT among patients with mild TBI, as well as the feasibility of 2 popular criteria (Canadian CT head rule [CCHR] and New Orleans Criteria [NOC]) in China. Patients with mild TBI who underwent cranial CT within 24 hours of the trauma were included in our institute. Two neurosurgeons reviewed the CT images independently to identify positive CT findings. The sensitivity and specificity of CCHR and NOC for positive CT findings related to TBI were analyzed. Finally, this study included 625 patients. Positive CT findings related to TBI were discovered in 13.12% (82/625) of these patients on cranial CT, and 6.88% (43/625) of them were admitted to the hospital for further management. Ultimately, 11 patients (1.76%, 11/625) underwent neurosurgery. In this study, the sensitivities of both the CCHR and NOC were 100%, but the specificity of CCHR was 43.36% and that of NOC was 33.12%. Based on our study, both CCHR and NOC have high sensitivity for the detection of positive CT findings related to head trauma in patients with mild TBI.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".