MétaCan
Menu
Back to cohort
Record W2127041298 · doi:10.4048/jbc.2014.17.2.107

The Educational Utility of Simulations in Teaching History and Physical Examination Skills in Diagnosing Breast Cancer: A Review of the Literature

2014· review· en· W2127041298 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Breast Cancer · 2014
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
FundersUniversity of Toronto
KeywordsBreast cancerMedicineMEDLINECurriculumRelevance (law)Medical physicsPhysical examinationMedical educationMedical historyCancerFamily medicineRadiologyInternal medicinePsychology

Abstract

fetched live from OpenAlex

This paper is a review of the literature examining the use of medical simulations to teach our future healthcare providers how to diagnose breast cancer. MEDLINE and Embase databases were searched to identify the literature published between 1990 and 2014. In total, 113 articles were retrieved and evaluated for their relevance to the topic. Simulation methods, such as standardized patients and breast models were found to enhance students' abilities to perform patient histories and physical examinations to detect breast cancer. In addition, simulation can help trainees learn how to communicate bad news to patients effectively. There is an abundance of literature supporting the continued use of simulations in the curricula of medical schools. However, future studies based on sound theoretical frameworks are needed to evaluate the positive effects of simulation-based education on patient outcomes.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.872
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.406
Teacher spread0.380 · 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