Concussion diagnosis and management: Knowledge and attitudes of family medicine residents.
Bibliographic record
Abstract
OBJECTIVE: To assess the knowledge of, attitudes toward, and learning needs for concussion diagnosis and management among family medicine residents. DESIGN: E-mail survey. SETTING: University of Toronto in Ontario. PARTICIPANTS: Family medicine residents (N = 348). MAIN OUTCOME MEASURES: tests were used as appropriate. Linear regression was used to compare self-reported concussion knowledge with knowledge scores. Thematic analysis was used to interpret answers to the qualitative question asking residents to describe challenges they foresee physicians facing when diagnosing and managing concussion. RESULTS: The residents who responded (n = 73, response rate 21%) correctly answered an average of 5.2 questions out of 9 (58%) regarding the diagnosis and management of concussion. Postgraduate year, sex, personal history of concussion, and clinical exposure to concussion were not significant factors in predicting the number of correct answers. Several misconceptions and knowledge gaps were revealed. Of residents who responded, 71% did not recognize chronic traumatic encephalopathy and only 63% recognized second-impact syndrome as consequences of repetitive concussions. Moreover, 32% of residents did not think that every individual with a concussion should see a physician as part of management. Knowledge scores did not predict self-reported concussion knowledge. Thematic analysis revealed 4 themes related to the challenges of concussion diagnosis and management: the nonspecificity and vagueness of symptoms, lack of formal diagnostic criteria, patient compliance with management, and counseling patients with respect to return to play, work, or learning. CONCLUSION: We found substantial gaps in knowledge surrounding concussion diagnosis and management among family medicine residents. This lack of knowledge should be addressed at both the undergraduate medical education level and the residency training level to improve concussion-related care and patient outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".