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Record W2944179283 · doi:10.5210/jbc.v42i2.9549

The Case of Ken Lowery: Visual Knowledge Building and Translation of Volumetric Radiographic Imagery for Dynamic 3D Medical Legal Visualization

2018· article· en· W2944179283 on OpenAlex
Amanda P. Miller, Leila Lax, Nick Woolridge, Anne Agur

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Biocommunication · 2018
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVisualizationRadiographyComputer scienceMedical imagingPresentation (obstetrics)WitnessMedical physicsMedicineRadiologyArtificial intelligence

Abstract

fetched live from OpenAlex

Advancements in medical imaging technology allow for the use of 3D volumetric radiographs in personal injury trials. Volumetric radiographs provide more comprehensive information than 2D imaging (i.e. CT scans/MRIs) but are more complicated for a judge and jury (non-medical audiences) to understand. The purpose of this medical legal visualization research project was to create and evaluate a plaintiff expert witness presentation that incorporates volumetric radiographs, combined with 3D anatomical models and animated sequences to improve understanding of complex medical information (e.g. to clarify the full extent of the traumatic brain injuries) and to obtain feedback on design strategies.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.380
Teacher spread0.363 · 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