Bibliographic record
Abstract
Robin’s Dermatology Case presents students with a 21-year-old female patient with an eyelid rash, which later turns out to be an occupation-related allergy. The main learning objectives are to explore some issues around skin conditions, their diagnosis and management while also learning about how information systems, such as electronic medical records (EMRs) and personal health records (PHRs), will increasingly influence the way we practice. 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 broken into three main sections – Robin’s initial visit, which includes the assessment of his symptoms, along with probing questions to determine possible causes and an initial diagnosis; choosing the correct type of follow-up testing; and finally, treatment and recommended lifestyle changes. The learner is encouraged to investigate, explore, ask questions, and make decisions based on realistic clinical encounters with the patient. Very little background about the patient is provided at the start of the case. As the learner moves through the case, the medical facts of the case are revealed. The learner will be required to engage general principles of history taking, consider principles of reflective practice, consider gender and occupation related challenges, and to consider the mental health implications for the care and treatment of a patient with a new allergy. The case has been tuned for the purposes of the AFMC Health Canada Infoway Competition on virtual patients, but is also part of a Resident Research Project at the University of Calgary Department of Family Medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".