Bibliographic record
Abstract
The Mildred Blonde Dizziness Case is designed to get learners thinking about how to approach a patient complaining of dizziness. Mildred is a 78 year old woman who is cheerful and friendly, despite giving vague answers to most of the questions that the she’s asked. The case gives students an opportunity to progress through various pathways, with some more direct than others. The main learning objectives are to 1) differentiate between dizziness and vertigo on history, 2) interpret the findings of the physical examination, including the Dix Hallpike maneuver, 3) provide a differential diagnosis for dizziness and vertigo and 4) describe initial workup and management of dizziness and vertigo. This case is broken into several main sections, starting with 1) Mildred’s initial complaint of dizziness and an opportunity to ask her questions; 2) an initial diagnosis (choose the best three) 3) reviewing Mildred’s current and recent medications for possible adverse effects 4) physical examination steps 5) updated diagnosis and 6) recommended management of symptoms. 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, see how their attitude towards the patient affects the outcome, and is challenged to bring new ideas and approaches to the care and treatment of a patient with dizziness. This case was developed as part of the CFPC SharcFM series.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".