Concussion Knowledge among Medical Students and Neurology/Neurosurgery Residents
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Concussion is a prevalent brain injury in the community. While primary prevention strategies need to be enhanced, it is also important to diagnose and treat concussions expertly and expeditiously to prevent serious complications that may be life-threatening or long lasting. Therefore, physicians should be knowledgeable about the diagnosis and management of concussions. The present study assesses Ontario medical students' and residents' knowledge of concussion management. METHODS: A survey to assess the knowledge and awareness of the diagnosis and treatment of concussions was developed and administered to graduating medical students (n= 222) and neurology and neurosurgery residents (n = 80) at the University of Toronto. RESULTS: Residents answered correctly significantly more of the questions regarding the diagnosis and management of concussions than the medical students (mean = 5.8 vs 4.1, t= 4.48, p<0.01). Gender, participation in sports, and personal concussion history were not predictive of the number of questions answered correctly. Several knowledge gaps were identified in the sample population as a whole. Approximately half of the medical students and residents did not recognize chronic traumatic encephalopathy (n = 36) or the second impact syndrome (n = 44) as possible consequences of repetitive concussions. Twenty-four percent of the medical students (n = 18) did not think that "every concussed individual should see a physician" as part of management. CONCLUSIONS: A significant number of medical students and residents have incomplete knowledge about concussion diagnosis and management. This should be addressed by targeting this population during undergraduate medical education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".