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Record W4408177112 · doi:10.1055/a-2537-2648

Patient Outcomes Following Reduction and Association of the Scaphoid and Lunate: A Retrospective Cohort Study

2025· article· en· W4408177112 on OpenAlexaff
Zoe E. Mack, Brodie Ritchie, Adina Tarcea, Gurpreet S. Dhaliwal, Neil J. White

Bibliographic record

VenueJournal of Wrist Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineDashLunateWristRetrospective cohort studyRadiographyCohortReduction (mathematics)Range of motionSurgeryLigamentInternal medicine

Abstract

fetched live from OpenAlex

Background: Scapholunate interosseous ligament (SLIL) injury is a common ligamentous injury of the wrist; however, the optimal operative management strategy remains unclear. The objective of this study was to investigate patient outcomes following the Reduction and Association of the Scaphoid and Lunate (RASL) procedure. Materials and Methods: Twenty-five consecutive patients who had an SLIL tear treated with RASL completed a demographic survey and three standardized patient-reported outcome tools (Disabilities of the Shoulder, Arm and Hand [DASH], Patient-Rated Wrist Evaluation [PRWE], and Patient Reported Outcome Measurement Information System, Upper Extremity [PROMIS] questionnaires). Standard wrist radiographs were taken preoperatively and postoperatively and bilateral wrist range of motion was measured. Results: At an average postsurgical time of 4.6 years, the average DASH score was 10.5 with a right-skewed distribution. There was no correlation between screw angle, preoperative scapholunate angle, or time from surgery and DASH score. Conclusion: We conclude that with meticulous surgical technique, patient reported and radiographic outcomes demonstrate the relative success of the RASL procedure as a viable option for SLIL reconstruction in appropriate candidates. Level of Evidence: Level IV evidence-a retrospective cohort study.

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)0.002
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 score0.004
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.005
GPT teacher head0.258
Teacher spread0.252 · 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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