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Record W4405323990 · doi:10.1016/j.jhsa.2024.10.015

Front-to-Back Arthroscopic Repair of Complete Lunotriquetral Ligament Injuries: A Case Presentation and Algorithm for Arthroscopic Management of Intercarpal Ligament Injuries

2024· article· en· W4405323990 on OpenAlexaff
Spencer B. Chambers

Bibliographic record

VenueThe Journal Of Hand Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsHand and Upper Limb ClinicWestern University
Fundersnot available
KeywordsMedicinePresentation (obstetrics)LigamentSurgeryArthroscopyAlgorithmComputer science

Abstract

fetched live from OpenAlex

The lunotriquetral intercarpal ligament (LTIL) is an important structure that equalizes the forces on the lunate imparted through the scapholunate intercarpal ligament. The extension moment of the triquetrum balances the flexion force of the scaphoid, positioning the lunate for efficient load transfer from the hand to the wrist. In contrast to the scapholunate intercarpal ligament, the LTIL is strongest volarly, with the most critical region being associated with the volar ulnocapitate ligament. Injury to the LTIL is less well understood in comparison to the scapholunate intercarpal ligament but is thought to arise from a fall on a radially deviated, flexed, and pronated wrist, or through attenuation related to ulnar impaction. The uncommon nature of this pathology has resulted in sparse literature, but it should always be considered in ulnar sided wrist pain. Arthroscopy is the gold standard tool for diagnosis, but treatments have been limited to thermal shrinkage or debridement, with reparative interventions classically being performed using an open approach. With advances in arthroscopic techniques, repairs and reconstructions are becoming possible and confer advantages such as less soft tissue stripping and accurate intra-articular joint evaluation. We demonstrate an arthroscopic repair of the volar and dorsal LTIL and propose a treatment methodology to incorporate arthroscopy into the treatment of these injuries.

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)0.002
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 score0.829
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.311
Teacher spread0.285 · 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 designOther design
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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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