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Record W3110102207 · doi:10.5210/jbc.v44i2.11493

Animating Primary Hemostasis for Medical Student Education

2020· article· en· W3110102207 on OpenAlex
Evelyn Lockhart, Michael Corrin, Paula James, Ric Lowe, Jodie Jenkinson

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.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Biocommunication · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
FundersUniversity of TorontoQueen's UniversityCurtin University of Technology
KeywordsStoryboardAnimationHemostasisComputer scienceMultimediaGraphicsMedicineComputer graphics (images)Surgery

Abstract

fetched live from OpenAlex

Physicians have difficulty recognizing and diagnosing disorders of primary hemostasis. The root of this may lie in their education, where students are often taught hemostasis using static graphics. We aimed to create a didactic animation on primary hemostasis for medical students to be used in North American medical schools. To promote widespread use of the animation, we surveyed hemostasis educators from Canada and the US on the animation’s learning objectives. The animation’s script and storyboard were developed using the Animation Processing Model (APM), a psychological processing model that addresses the perceptual limitations of learners. This animation is the first biomedical animation to use the APM in its design. Furthermore, this is the first didactic hemostasis animation which sought peer consensus for its learning objectives.

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)0.000
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 score0.907
Threshold uncertainty score0.300

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 science0.0010.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.425
Teacher spread0.370 · 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