Bibliographic record
Abstract
Emil (Palliative Care) Case allows students to work through a realistic patient visit and subsequent interactions with Emil, a 72 year old male with an 8 month history of non curable non small cell lung cancer. The main learning objectives are to explore some issues surrounding end of life care such as understanding appropriate Palliative Care Symptom Management, exploring the different components of a goals of care discussion for advance care planning with a Palliative Care patient and his/her family and increasing the comfort level for discussing what to expect as death nears with the patient and family. This case is broken into three main sections – Emil's initial visit, which includes the assessment of his symptoms and an initial diagnosis; the therapy that you choose thereafter; and a follow-up visit after one month. 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 be challenged to bring new ideas and approaches to the care and treatment of a patient with depression. This case is part of a series being generated for the CFPC SharcFM series. This particular case deals with various Palliative Care issues in Family Medicine. Follow Emil's case as he goes through a series of struggles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".