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Integrative Teaching of Breast Histology in Clinical Case‐Based Learning

2016· article· en· W2587035595 on OpenAlexafffund
Gregory J. Naus, Diana Beşliu-Ionescu, Kuo‐Hsing Kuo

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British ColumbiaBC Cancer Agency
FundersUniversity of British Columbia
KeywordsHistologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

The objective of integrative teaching of breast histology aims to improve learning efficiency for medical students in both content knowledge and clinical application. The traditional content structure of Histology is designed to assist students in acquiring in‐depth knowledge of cell and tissue in a systemic order. However, current trends in medical curriculum are headed towards clinical presentation‐based formats to better assist students’ in developing critical thinking abilities under clinical settings. The system‐based content structure of Histology has therefore required revision to focus on presenting content relevant to clinical applications. The topic of breast pathology was identified for a pilot project to develop an integrative model of histology teaching. In the traditional system‐based structure, the topic of breast histology was either delivered within the systems of reproduction or endocrine, which lacked correlation with clinical applications and negatively impacted the effect of learning. With the integration of breast pathology, the learning experience of breast histology is expected to be more meaningful. A team of histologists and breast pathologists developed an integrative module of breast histology and pathology, incorporating content knowledge from histology and pathology for a clinical approach. The delivery of the integrative module contains two stages: (1) on‐line self‐learning and (2) clinical‐case discussion, which are designed to fulfill the purpose of developing content knowledge, while further fostering critical thinking. The on‐line component feature the designs of outcome‐based learning with escalating levels of knowledge. The outcome‐based design aims to prepare students with competent levels of knowledge for case discussion and to further enhance learning experience during the clinical application. The design of escalating knowledge levels allows students to learn based on individuals’ learning pace as well as allowing for real‐time review of fundamental material. The integrative teaching model is currently undergoing the piloting phase. Support or Funding Information The Teaching and Learning Enhancement Fund/University of British Columbia

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.283
Teacher spread0.268 · 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 designOther design
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
Published2016
Admission routes2
Has abstractyes

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