Lehetne-e csökkenteni az enyhe koponyasérültek sürgősségi koponya-CT-vizsgálatainak számát?
Bibliographic record
Abstract
Introduction: Skull and brain injuries (craniocerebral traumas) should be classified according to internationally accepted standards, their frequency and distribution varies from country to country. The frequency of skull and brain injuries in Hungary varies about 2,000 skull injuries per 100,000 inhabitants. No more than a quarter of them involve hospitalization. The number of CT examinations performed in the United States and in our country has doubled in the past 20–30 years. Nearly 90% of the skull CT scans are negative. Patients with minimal head injuries do not experience loss of consciousness or other neurological changes and have GCS values of 13–15. Following observation, the majority of patients with these minor injuries could be discharged without any consequences. Objective: The inefficient use of CT examinations significantly increases unnecessary radiation doses and health care costs. To mitigate these, there are several well-proven regulatory systems in force abroad. However, their use has not yet become a routine in our country. Our aim was to investigate how the number of head CT scans in our emergency unit could have been reduced. Method: In this study, we examined the method of care for patients with cranial injuries presenting at the Békés County Emergency Department. Results: Results of this retrospective analysis, compared with the Canadian Cranial CT Rules, suggest that the number of urgent cranial CT examinations could have been reduced by 70%. Conclusion: Applying the standard systems that have already been efficient abroad, it would be significantly possible to improve the efficiency of care for minor head injuries in Hungarian emergency practice as well. Orv Hetil. 2024; 165(14): 538–544.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".