The Relationship between Risk Factors of Head Trauma with CT Scan Findings in Children with Minor Head Trauma Admitted to Hospital
Bibliographic record
Abstract
In Emergency Medicine for determining the intracranial injury (ICI) in children with head trauma, usually Brain CT Scan is performed. Since Brain CT Scan especially in children has some disadvantages, it is ideal to find a method which could help to choose only the children with real head trauma injury for Brain CT Scan. This study was descriptive, Analytic and Non interventional. We reviewed the archived files of children with head trauma injuries admitted in emergency department of Imam Hossein hospital within two years. Patient’s CT scan findings and head trauma risk factors were evaluated in this study. Out of 368 patients, 326 patients had normal Brain CT Scan. 28 of them showed signs of ICI consisting intraventrucular hemorrhage (IVH), Contusion, subarachnoid hemorrhage (SAH), subdural hemorrhage (SDH), epidural hematoma (EDH), and Pneumocephalus. Twenty-seven patients showed Skull FX, which14 of them had Isolated Fracture, and 13 of them showed also signs of ICI. Since, patients with isolated FX usually discharge quickly from Emergency Department; their data did not include in outcome of this study. The Patients has been divided into two groups: 1- ICI, 2- without ICI. RR (relative risk), CI (Confidence interval) and sensitivity, positive predictive value (PPV), negative predictive value (NPV) and association of these risk factors with ICI were assessed with Chi-2 test. In the end to determine the indications of CT scan, presence of one of these five risk factors is important including: Abnormal Mental Status, Clinical signs of Skull FX, history of vomiting, Craniofacial Soft Tissue Injury (including Subgaleal Hematomas or Laceration) and Headache. For all other patients without these risk factors, observation and Follow Up can be used which has more advantages and less cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".