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Record W4307955085 · doi:10.1016/j.jhsg.2022.10.001

Radiographic Evaluation of Carpal Mechanics and the Scapholunate Angle in a Clenched Fist with Dynamic Computed Tomography Imaging

2022· article· en· W4307955085 on OpenAlexaff
Alexandra Munn, Andrew Furey, John G. Hopkins, Nick Smith, Nicholas Chang, Daniel Sai Squire

Bibliographic record

VenueJournal of Hand Surgery Global Online · 2022
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWristUlnar deviationMedicineRange of motionFistRadiographyNuclear medicineAnatomyRadiology

Abstract

fetched live from OpenAlex

PurposeThe long-term consequences of injuries to the scapholunate joint can severely limit hand function, and the potential for posttraumatic deformity makes early recognition of these injuries important. The purpose of this study was to evaluate the motion of the scapholunate joint in normal wrists through the radial and ulnar deviation using novel dynamic computed tomography (CT) imaging.MethodsFifteen participants consented to have their uninjured wrists scanned. A protocol was designed to ensure adequate time, yet limited exposure, for volunteers. Participants began with the hand in a relaxed fist position and then proceeded to clench the hand in a full fist and relax. Once relaxed again, the wrist was maximally ulnarly deviated and then maximally radially deviated in a fluid motion. Dynamic CT imaging was captured throughout the range of motion.ResultsThe scapholunate angle was measured on dynamic wrist images. The mean range of the scapholunate angle that the wrists moved through was 37.2°–45.9°, and the mean midpoint angle was 41.2° ± 0.4°. All wrists had small, measurable differences in the scapholunate angle when moving from the maximum ulnar deviation to the maximal radial deviation. The average maximum angle change through the range is 11.7°, whereas the average minimum angle change was 0.9°.ConclusionsIn this study, scapholunate angle calculations using dynamic wrist CT scans were within the range of accepted normal for the angle in uninjured wrists. With the increased focus on dynamic imaging for wrist motion, it may be possible to derive a standardized protocol for mapping the carpal motion that is clinically applicable and reproducible.Type of study/level of evidenceDiagnostic IV. The long-term consequences of injuries to the scapholunate joint can severely limit hand function, and the potential for posttraumatic deformity makes early recognition of these injuries important. The purpose of this study was to evaluate the motion of the scapholunate joint in normal wrists through the radial and ulnar deviation using novel dynamic computed tomography (CT) imaging. Fifteen participants consented to have their uninjured wrists scanned. A protocol was designed to ensure adequate time, yet limited exposure, for volunteers. Participants began with the hand in a relaxed fist position and then proceeded to clench the hand in a full fist and relax. Once relaxed again, the wrist was maximally ulnarly deviated and then maximally radially deviated in a fluid motion. Dynamic CT imaging was captured throughout the range of motion. The scapholunate angle was measured on dynamic wrist images. The mean range of the scapholunate angle that the wrists moved through was 37.2°–45.9°, and the mean midpoint angle was 41.2° ± 0.4°. All wrists had small, measurable differences in the scapholunate angle when moving from the maximum ulnar deviation to the maximal radial deviation. The average maximum angle change through the range is 11.7°, whereas the average minimum angle change was 0.9°. In this study, scapholunate angle calculations using dynamic wrist CT scans were within the range of accepted normal for the angle in uninjured wrists. With the increased focus on dynamic imaging for wrist motion, it may be possible to derive a standardized protocol for mapping the carpal motion that is clinically applicable and reproducible.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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 designObservational
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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Citations3
Published2022
Admission routes1
Has abstractyes

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