Prevalence of Pathological Lesion Due to Mild Head Trauma in Computed Tomography Scan of Patients’ Brains
Bibliographic record
Abstract
Background and aims: This study investigated the prevalence of pathological lesions on the computed tomography (CT) scans of the brains of patients with mild head trauma based on the New Orleans-Canadian criteria at Shahrekord Ayatollah Kashani Hospital, Iran. Methods: All patients referred to the Emergency Department of Shahrekord Ayatollah Kashani Hospital in 2019 with a history of head trauma were included in this cross-sectional, descriptive-analytical study according to the criteria of mild head trauma. Then, the relevant checklist was used to record the patients’ level of consciousness, demographic information, and cause of trauma. Finally, the data were analyzed using SPSS 18, and the patient’s lesions were reported accordingly. Results: Out of 143 patients, 89 were males, and 54 were females in this study. Falling from a height was the cause of head trauma in most patients (43.3%). Among all patients, the CT scans of six patients were abnormal and had lesions. The vomiting had a significant relationship with the results of the CT scan, and for patients with mild head trauma, the Canadian and New Orleans indices had the same clinical importance. Conclusion: According to the results of the present study, the New Orleans index could identify more patients as CT scan candidates than the Canadian index; however, there was no difference in the final result (the presence of a pathological lesion in the CT scan) between these two indices. The New Orleans index has more features than the Canadian index, but its results are not different from the Canadian index. Thus, we believe that using the Canadian index can reduce imaging rates, costs, and protection from the side effects of radiation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".