Efficacy of the Canadian CT Head Rule in Patients Presenting to the Emergency Department with Minor Head Injury
Bibliographic record
Abstract
Introduction: Approximately, one in three computed tomography (CT) scans performed for head injury may be avoidable. We evaluate the efficacy of the Canadian CT head rule (CCHR) on head CT imaging in minor head injury (MHI) and its association of Glasgow Coma Scale (GCS) and structural abnormality. Materials and methods: We conducted a prospective cross-sectional study from May 2018 to October 2019 in the Department of Emergency Medicine, Pushpagiri Institute of Medical Sciences and Research Centre, Thiruvalla, Kerala. The CCHR is applied to patients with MHIs (GCS 13-15) after initial stabilization and it is ascertained, if they require a non-contrast CT head and imaging is done. For those who do not require CT head as per the CCHR are excluded from this study. After imaging the patients who have a positive finding on CT head are admitted and followed up if they underwent any neurosurgical intervention, those with no findings in CT head are discharged from the hospital. A total of 203 patients were included during study period. Results: A total of 203 patients were included in study with mean age of 49.5 years. Approximately, 70% (142) were male. Sensitivity of CCHR for predicting positive CT finding in the present study sample was 68% and specificity was 42.5%. Conclusion: Canadian CT head rule is a useful tool in the Emergency Department for predicting the requirement of CT in patients with MHI. Canadian CT head rule can reduce the number of CT scans ordered following MHI in ED, thus improving the healthcare costs. How to cite this article: Reddy A, Poonthottathil F, Jonnakuti R, Thomas R. Efficacy of the Canadian CT Head Rule in Patients Presenting to the Emergency Department with Minor Head Injury. Indian J Crit Care Med 2024;28(2):148-151.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".