USING ALGORITHMS AND ACCREDITED EDUCATION SESSIONS TO ADDRESS PHYSICIAN KNOWLEDGE GAPS REGARDING TRANSIENT ISCHEMIC ATTACK (TIA) DIAGNOSIS AND TREATMENT IN NOVA SCOTIA
Bibliographic record
Abstract
IntroductionTIA patients access care through their local emergency department (ED) or primary care physician when experiencing stroke symptoms. Provincial and local data revealed there was an opportunity to increase knowledge and awareness of the Canadian Stroke Best Practices for the diagnosis and treatment of TIA among physicians across the province.MethodsTwo TIA algorithms, one for ED and one for primary care, were developed based on the Canadian Stroke Best Practices covering the areas of diagnosis, risk assessment, tests/evaluation, and medications. The algorithms were reviewed by a wide range of stroke team members and physicians across the province. To support the implementation and use of the algorithms, an accredited Continuing Medical Education (CME) program was developed. The learning objectives were: increase knowledge in diagnosing TIA and the investigations required, improve comprehension of TIA risk categorization, and apply learning through case studies. Evaluations were completed at the end of each session.ResultsA total of 880 algorithms were disseminated (46 ED; 834 Primary Care) throughout Nova Scotia. Eleven CME sessions were held province-wide with a total of 140 healthcare providers attending. Session evaluations were completed by 107 attendees: 96% agreed the session met the stated objectives, 92% reported the session enhanced their knowledge, and 82% indicated they planned to use the TIA algorithm in their practice.ConclusionAlgorithms and an accredited CME program developed to meet specific knowledge gaps are effective methods to educate and support physicians in the diagnosis and treatment of TIA patients.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.026 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".