Heads up for concussion, what do emergency physicians know? A scoping review
Bibliographic record
Abstract
INTRODUCTION: Concussion is a common condition, with sources estimating between 1.2% and 6.6% of all ED presentations are related to head injury, and concussion has significant healthcare costs. In April 2023, the UK Government published guidelines for diagnosis and management of concussions in grassroots sport, recommending anyone that has sustained a suspected concussion has a same day review by an appropriate healthcare professional. It is therefore essential that emergency medicine physicians have the required knowledge and use current clinical practice guidelines in managing suspected concussions presenting to their departments. This scoping review aims to review the current literature regarding concussion knowledge, diagnosis and management amongst emergency physicians. MATERIALS AND METHODS: This scoping review was conducted using the six-step process laid out by Arksey and O'Malley and included 17 papers from January 2012 to February 2023, identified by searching 5 online databases (MEDLINE, Embase, Web of Science, Scopus and Google Scholar) in February 2023 alongside a hand search of references. Search terms relevant to concussion, emergency medicine and medical education were used. RESULTS: 14 of the 17 papers originated from North America, all studies utilised either an online survey or chart review methodology. 3 papers included an educational intervention. 12 studies looked at all grades of EMPs. 14 of the studies highlighted knowledge gaps amongst EMPs, the 3 that did not specifically mention this were the 3 interventional studies. CONCLUSION: EMPs have large knowledge gaps regarding concussion and limited adherence to current guidelines. Efforts should be made at improving these results amongst EMPs. Further research is needed to find the most beneficial and cost-effective approach to improving concussion knowledge of concussion diagnosis and management in EDs, particularly within the UK.
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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 | 0.017 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".