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Record W4282838963 · doi:10.1503/cjs.023420

Four-dimensional computed tomography: musculoskeletal applications

2022· review· en· W4282838963 on OpenAlexafffundvenue
Murray T. Wong, Charmaine Wiens, Michael T. Kuczynski, Sarah L. Manske, Prism Schneider

Bibliographic record

VenueCanadian Journal of Surgery · 2022
Typereview
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchOrthopaedic Trauma AssociationUniversity of CalgaryNatural Sciences and Engineering Research Council of CanadaOsteoporosis CanadaAmgen
KeywordsMedicineModality (human–computer interaction)Orthopedic surgeryRange of motionWristAnkleKinematicsElbowComputed tomographyReduction (mathematics)RadiologyMedical physicsArtificial intelligenceSurgeryComputer science

Abstract

fetched live from OpenAlex

Four-dimensional computed tomography (4DCT), or dynamic CT, is an emerging modality with a wealth of orthopedic applications for both clinical practice and research. This technology creates CT volumes of a moving structure at multiple time points to depict real-time motion. Recent advances in acquisition technology and reduction in radiation dosage have allowed for increased adoption of the modality and have made imaging of joint motion feasible and safe. Musculoskeletal 4DCT has been used primarily to investigate wrist motion; however, the utility of 4DCT has been shown in other areas, including the shoulder, elbow, hip, knee and ankle. Imaging these joints through a full range of motion provides new insight into dynamic phenomena such as instability, impingement and joint kinematics. Although 4DCT has not yet been widely adopted in orthopedic practice and research, future use has the potential to enable a deeper understanding of musculoskeletal conditions and to improve patient care.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.262
Teacher spread0.212 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations14
Published2022
Admission routes3
Has abstractyes

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