Awareness and use of the Canadian computed tomography head rule for mild head injury patients among Chinese emergency physicians
Bibliographic record
Abstract
Objective: Computed tomography (CT) scan has been an increasingly essential diagnostic tool for emergency physicians (EPs) to triage emergency patients. Canadian computed tomography Head Rule (CCHR) had been established and widely used to spare patients with mild head injury from unnecessary radiation. However, the awareness of CCHR and its actual utilization among Chinese EPs were unknown. This survey was to investigate the awareness and use of CCHR and their associated characteristics among Chinese EPs.Methods: Questionnaire was randomly sent to EPs from different Chinese hospitals. Surveyed EPs were asked how well they know about the CCHR and how often they use the CCHR to guide head CT use. Association between the awareness and utilization of CCHR and the physicians’ characteristics were analyzed using repeated-measures logistic regression.Results: About 41.7% of the total 247 responders noted they “very familiar” or “somewhat familiar” with CCHR while the utilization rate was 24.7%. With respect to the most important underlying barriers for the use of CCHR, approximate half (48.5%) cited “fear of malpractice” as the leading cause. “Received specific training regarding radiation dose of CT” was the significant predicting factor both for the awareness (OR 5.87; 95% CI, 3.08-11.21) and the use (OR 6.10, 95% CI, 2.91-12.80) of CCHR.Conclusions: Fear of malpractice and lack of radiation risk knowledge were two main barriers to apply CCHR in the request of CT for patients with mild head injury. Furthermore, EPs with specific training about radiation risk of CT were more likely to know and use of CCHR. doi: http://dx.doi.org/10.12669/pjms.294.3469How to cite this:Huang X, Zhou JC, Pan KH, Zhao HC. Awareness and use of the Canadian computed tomography head rule for mild head injury patients among Chinese emergency physicians. Pak J Med Sci 2013;29(4):951-956. doi: http://dx.doi.org/10.12669/pjms.294.3469This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".