Two Different Approaches in Obtaining Head Computerized Tomography Scan in Minor Head Injuries
Bibliographic record
Abstract
Background: The management of minor head injury (GCS score of 15) especially in the use of computed tomography (CT) scan is still controversial. As a big and developing country, Indonesia faced some problems in the management of minor head injuries. Those problems were limited number of CT scan, big number of minor head injured patients assessed in emergency unit and far distance between small cities and referral centers. This study was aimed to provide different approaches in obtaining CT scan in this group of patients. Methods: This was a cohort prospective study involving 364 head injured patients with a GCS score of 15, aged over six years. All studied clinical data were recorded and CT scan was obtained. The relationship between the clinical risk factors and the presence of abnormal CT scan (the first end point of this study) and the need for surgery (the second end point) were tested by univariate analysis ((X 2 -test). Logistic regression analysis was then used to find the best combination of these clinical factors that were highly sensitive to detect abnormal CT scan and the need for surgery. Results: The incidence of abnormal CT scan and the need for surgery were 13.2% and 3.7% respectively. Loss of consciousness (LOC) ( R R 4 . 84, 95 % CI 1 . 29 - 18 . 13) , amnesia ( R R 4 . 45, 95% CI 1 . 86 - 10 . 68), cranial soft tissue injury ( R R 8 . 56, 95% CI 3 . 43 - 21 . 46), skull fracture ( R R 6 . 81, 95% CI 2 . 04 - 22 . 77), age > 60 years ( R R 5 . 56, 95% CI 2 . 09 - 14 . 77) were significant clinical factors of abnormal CT scan. While amnesia ( R R 0 . 068, 95% CI 0 . 007 - 0 . 626), cranial soft tissue injury ( RR 0 . 076, 95% CI 0 . 009 - 0 . 647) and skull fracture ( R R 0 . 145, 95% CI 0 . 035 - 0 . 607) were significant clinical factors of the need for surgery . Conclusion: Our recent study provided two different approaches in obtaining head CT scan in minor head injuries, which were dependent on the availability of CT scan and the aim of taking CT scan. J Neurol Res. 2013;3(3-4):114-121 doi: https://doi.org/10.4021/jnr225w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".