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Record W3013120382 · doi:10.1017/s1047951120000852

Heart University: a new online educational forum in paediatric and adult congenital cardiac care. The future of virtual learning in a post-pandemic world?

2020· article· en· W3013120382 on OpenAlexaff
Justin T. Tretter, Jonathan Windram, Theresa Faulkner, Michelle Hudgens, Skaistė Sendžikaitė, Nico A. Blom, Katarina Hanséus, Rohit S. Loomba, Colin J. McMahon, Bistra Zheleva, Raman Kumar, Jeffrey P. Jacobs, Erwin Oechslin, Gary D. Webb, Andrew N. Redington

Bibliographic record

VenueCardiology in the Young · 2020
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsUniversity of TorontoUniversity Health NetworkUniversity of Alberta HospitalAlberta Hospital Edmonton
FundersCincinnati Children's Hospital Medical Center
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)Online learningHeart diseaseMedical educationDiseaseMultimediaInfectious disease (medical specialty)CardiologyInternal medicine

Abstract

fetched live from OpenAlex

Online learning has become an increasingly expected and popular component for education of the modern-day adult learner, including the medical provider. In light of the recent coronavirus pandemic, there has never been more urgency to establish opportunities for supplemental online learning. Heart University aims to be "the go-to online resource" for e-learning in CHD and paediatric-acquired heart disease. It is a carefully curated open access library of paedagogical material for all providers of care to children and adults with CHD or children with acquired heart disease, whether a trainee or a practising provider. In this manuscript, we review the aims, development, current offerings and standing, and future goals of Heart University.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.007
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0240.003

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations49
Published2020
Admission routes1
Has abstractyes

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