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Record W3209710578 · doi:10.1097/sih.0000000000000614

Creation of a Novel Hands-on Model to Teach Breast Tanner Staging to Pediatric Learners

2021· article· en· W3209710578 on OpenAlexaff
Rachel Kadakia, Ellie O’Brien, Reema L. Habiby

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2021
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsContext (archaeology)MedicineCurriculumMedical educationPediatricsPsychologyPedagogy

Abstract

fetched live from OpenAlex

INTRODUCTION: Pubertal Tanner staging is a standard part of the pediatric physical examination and provides valuable insight into a child's growth and development. In practice, pediatric care practitioners have varying levels of confidence and expertise with Tanner staging. Currently, breast Tanner staging is taught via illustrated images or limited hands-on practice on real patients during pediatric residency training. METHODS: We used synthetic materials to develop a lifelike, 3-dimensional, hands-on educational tool aimed at teaching medical students and pediatric resident physicians how to identify and distinguish among the 5 breast Tanner stages. This tool was evaluated by a group of experienced pediatric endocrinologists. RESULTS: Thirty pediatric endocrinologists with an average of 16.7 years of clinical experience evaluated the model, and all participants believed the model was a valuable teaching tool for medical students and pediatric resident physicians. Tanner stages 1, 2, 3, 4, and 5 were correctly identified by 100%, 93%, 90%, 100%, and 73% of participants, respectively. CONCLUSIONS: We show that the use of a synthetic, 3-dimensional, lifelike breast model to teach breast Tanner staging may be valuable within the context of pediatric medical education. Further refinement of the model as well as curriculum development and evaluation is necessary before broadly disseminating this model as an educational tool.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.347
Teacher spread0.312 · 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 designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueSimulation in Healthcare The Journal of the Society for Simulation in HealthcareSame topicAnatomy and Medical TechnologyFrench-language works237,207