Bibliographic record
Abstract
Catherine’s Asthma Case introduces a 52-year-old woman with a persistent cough as the student’s last patient of the day. The main learning objectives are to take a focused history and identify risk factors and clinical features that increases probability of asthma diagnosis; to develop a broad differential diagnosis of wheeze / cough. (Not all that wheezes is asthma); to become familiar with management of asthma, different medication delivery methods, and other management options; to be comfortable assessing asthma control in follow-up; and to be aware of inhaled glucocorticoid side effects. There is more information about our learning objectives and the project's aims here on the Canadian Health Education Commons (CHEC).The EMR's used in our case include MedAccess and Netcare. This case is designed to make the student consider clinical time management in their decision-making. The student is given 15 minutes to make all of their decisions for the initial visit, including history and patient chart review, a physical exam, an initial diagnosis, initial management and patient education. This visit is followed up 2 months later, and the student is asked whether the symptoms have been controlled. This decision can lead to two separate outcomes for the patient. There is also bonus content for this case, with further management several months later. The learner is encouraged to investigate, explore, ask questions, and make rapid decisions based on realistic clinical encounters with the patient. The learner will be required to engage general principles of history taking, consider principles of reflective practice, see how their assessment may impact a patient’s health following the visit, and consider some of the fears or misconceptions around steroid use. This case was developed as part of the SharcBAIT Project
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".