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Record W2331767746 · doi:10.15766/mep_2374-8265.1683

Gyne-Oncology Interactive Problem-Based Cases for Postgraduate Residents

2010· article· en· W2331767746 on OpenAlexaff
Leslie Sadownik, Sarah Finlayson

Bibliographic record

VenueMedEdPORTAL · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineCervixMedical educationObstetrics and gynaecologyDisadvantageFamily medicineOncologyPregnancyCancerInternal medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This educational resource is a series of five interactive problem-based cases that focus on the management of patients presenting with cancers of the female reproductive tract (cancer of the cervix, endometrium, vulva, ovary and gestational trophoblastic disease). Each case was originally designed to be used in an academic half-day 3 hours in length and facilitated by a content expert. However, the cases can be used by learners independently or in a group. Questions in the cases facilitate the diagnostic and clinical reasoning skills of residents. Ideally the cases can be enhanced by adding appropriate clinical, histology, and pathology images. The cases were designed and delivered as part of a medical education research project analyzing what type of learning was taking place in the protected time set aside for resident learning. Prior to the introduction of these cases, the formats of the educational sessions were primarily didactic (PowerPoint presentations). Although residents reported enjoying these sessions, they did not prepare for them, interact during them, or reflect on them. After these academic sessions were changed to case-based discussion, the residents uniformly reported a significant increase in self-directed study behaviors before, during, and after the sessions.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.464
Teacher spread0.417 · 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 designNot applicable
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".

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Citations1
Published2010
Admission routes1
Has abstractyes

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