Age-appropriateness of decision for brain CT scan in elderly patients with mild traumatic brain injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a prevalent issue among patients presenting in emergency departments (EDs), with mild TBI being the most common form. [1,2]Mild TBI is characterized by symptoms such as loss of consciousness, amnesia, disorientation, or a Glasgow Coma Scale (GCS) score of 13-15. [3]Although most patients with mild TBI can be safely discharged, Yuksen et al [4] reported that about 14.12% of all mild TBI patients were found to be positive for intracranial hemorrhage on a head CT scan.Computed tomography (CT) scans are widely used as a diagnostic tool for TBI, as they provide a quick and reliable diagnosis. [5]linical factors associated with an increased risk of intracranial bleeding on a CT scan include headache, altered consciousness, neurological deficits, posttraumatic vomiting and amnesia, and signs of skull or basilar skull fractures. [4,6]To determine the appropriate use of CT scans, various clinical guidelines have been evaluated, such as the Canadian CT Head Rules (CCHR), New Orleans Criteria (NOC), National Emergency X-ray Utilization Study II (NEXUS II), and the mild TBI risk score. [6]CCHR has a higher specifi city (39.7% vs. 5.6%), positive predictive value (PPV), and negative predictive value (NPV) than NOC. [7]he global population is rapidly aging due to declining birth rates and increased life expectancy.By the year 2050, the proportion of older adults is projected to reach 21.1%. [8] TBI is common in this aging population, with falls being the most common cause.In older adults, there is a higher incidence of subdural and intraparenchymal hematomas due to decreased brain mass, increased stretching and tension of the bridging veins, and brain atrophy, which can allow blood accumulation without initial signs or symptoms.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".