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Record W2766638288 · doi:10.1017/9781316476864

OSCE Guide for the ABA Applied Examination

2017· book· en· W2766638288 on OpenAlexaff
Sean Neill, William Simpson, Andrew Davies, Peter Frank, Simon Maguire, Milo Engoren

Bibliographic record

VenueCambridge University Press eBooks · 2017
Typebook
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsObjective structured clinical examinationProcess (computing)CurriculumTest (biology)Resource (disambiguation)Medical educationComputer scienceSample (material)PsychologyMedicinePedagogy

Abstract

fetched live from OpenAlex

The OSCE (Objective Structured Clinical Examination) component of the ABA Applied exam is a new concept that involves a number of 'live' practical stations in which candidates must demonstrate communication, professionalism and technical skills to the examiners. This book covers topics that are outlined in the ABA curriculum, presented in a way that emulates the OSCE exam setting, and will help candidates prepare for the exam and test their knowledge. Each station is constructed in clear, logical fashion to make the revision of individual topics more accessible. The sample questions and answers allow self-testing and are complemented by discussions, numerous illustrations and up-to-date clinical guidelines which follow modern-day anesthetic practice. The OSCE Guide for the ABA Applied Examination is a must-have resource which will help candidates further understand and prepare for the OSCE process.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.282
Threshold uncertainty score0.942

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2820.246

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.

Opus teacher head0.026
GPT teacher head0.240
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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