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Record W4319316705 · doi:10.4103/ehp.ehp_27_22

Objective Structured Clinical Examination Case Writing

2023· article· en· W4319316705 on OpenAlexaff
Fok‐Han Leung, Giovanna Sirianni, Kulamakan Kulasegaram

Bibliographic record

VenueEducation in the Health Professions · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsObjective structured clinical examinationComputer sciencePsychologyMedicineMedical education

Abstract

fetched live from OpenAlex

The Objective Structured Clinical Examination (OSCE) is a commonly utilized modality to assess learner clinical skills in a simulated environment. It is important that OSCE cases are well written; A poorly constructed case can frustrate the learner, lack realism, and lead to disrupted narrative flow. An ideal response process in an OSCE case, or for any assessment, takes the learner to the patient’s bedside or puts them into the cognitive and affective state similar to that of clinical work. There are several parallels between writing OSCEs and creating Dungeons and Dragons (D&D) adventures. From determining the central conflict to designing D&D adventures using the classic three part structure, from creating challenging yet solvable challenges to distributing loot and treasure, the approaches and lessons of being a DM align with being an OSCE case writer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.107
GPT teacher head0.557
Teacher spread0.449 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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