A review of minor traumatic brain injury presentations and their management in Brisbane emergency departments
Bibliographic record
Abstract
Objective A retrospective audit of minor traumatic brain injury presentations to three Brisbane emergency departments aiming to assess rates of CT scans and compliance with the Canadian CT Head Rule (CCTHR), as well as reviewing the demographics of patients and their management. Method Minor traumatic brain injury presentations to the Emergency departments of the Royal Brisbane and Women’s Hospital, Redcliffe Hospital and Queen Elizabeth II Hospital between July 1st and August 30th, 2019 were identified via diagnosis searches in patient tracking systems. Data collected included patient demographics, use of CT scans and Abbreviated Westmead Post Traumatic Amnesia Scale assessment, length of stay and discharge advice regarding return to sport. Results 200 minor traumatic brain injury presentations were included. 75% of patients received a CT head with only 9% of these found to be non-compliant with the CCTHR. The most common indications for CTs were age >65, GCS <15 at 2 hours post injury and anticoagulation. Only 40% of patients that qualified for A-WTPAS received the assessment. 18% of presentations were due to sporting injuries and 69% were not given return to play advice. The average length of stay was 250 minutes with 41.5% of presentations longer than 4 hours. 99% of the patients were discharged home from the ED with 1% admitted. Conclusion The study characterised minor traumatic brain injury management across three emergency departments and showed that most patients received CT head scans that were indicated. However, other areas of management such as A-WPTAs and return to play advice must be improved.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".