Bibliographic record
Abstract
The FRCEM OSCE Revision Guide is an excellent resource for trainees who are preparing for their Fellowship of the Royal College of Emergency Medicine (FRCEM) Objective Structured Clinical Examination (OSCE). Written by three UK-trained Emergency Medicine Consultants, this book provides an insight into the preparation required to pass the exam. The FRCEM OSCE has somewhat evolved over recent years, and so the authors explain the structure and format of the current exam. They also provide useful tips on revision strategy, as well as on how to navigate the actual exam. Each element of the OSCE is covered throughout the chapters: history, examination, teaching skills, practical skills and procedures, communication skills, resuscitation scenarios, psychiatry cases, and management scenarios. Each chapter provides numerous OSCE scenarios with instructions for the candidate and clear mark schemes, as well as learning points for the trickier cases. The cases reflect real-life scenarios that are common, yet complex presentations to the emergency department. They are pertinent to the Royal College of Emergency Medicine (RCEM) curriculum and have frequently appeared in previous OSCEs. The cases are often nuanced in such a way as to stretch the candidate to communicate and make decisions at the level of a senior registrar or consultant. The mark schemes are based on best practice and up-to-date national guidance. Overall, this book provides a great structure for successful revision.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.240 | 0.179 |
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".