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Record W4411347933 · doi:10.1177/17531934251348829

Radiocarpal and midcarpal joint congruency after perilunate dislocations and fracture-dislocations: a cross-sectional study

2025· article· en· W4411347933 on OpenAlexaff
Eric C. Mitchell, Lauren Straatman, Emily Lalone, Ruby Grewal

Bibliographic record

VenueJournal of Hand Surgery (European Volume) · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsMedicineRadiographyWristVisual analogue scaleSurgeryScaphoid fractureJoint (building)OsteoarthritisOrthodonticsNuclear medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate carpal joint contact patterns after perilunate injuries and potential associations between functional and radiographic outcomes. Twenty-two patients with a computed tomography (CT) scan at least 2 years postoperatively were reviewed (mean follow-up 15 years). Assessment of carpal degenerative changes was done using Kellgren–Lawrence grading and CT-derived joint space area, which was calculated as the total area with interbone distance less than 2 mm. Increased joint space area signified a greater area with joint space narrowing and cartilage loss. Fifteen patients had severe joint space narrowing at the scaphocapitate and capitolunate joints. Nine and seven patients had severe narrowing at the radiolunate and radioscaphoid joints, respectively. Degenerative changes did not follow a typical scapholunate advanced collapse pattern. Increased scaphocapitate joint space area was associated with worse Patient Rated Wrist Evaluation scores ( r = −0.47, p = 0.02) and visual analogue scale pain scores ( r = −0.44, p = 0.03). This study suggests that patients with more severe degenerative changes after perilunate injuries may have worse functional outcomes. Level of evidence: IV

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)0.001
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 score0.008
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.278
Teacher spread0.263 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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