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Record W4411306757 · doi:10.1016/j.xrrt.2025.05.022

Predicted impingement-free motion amplitudes in reverse total shoulder arthroplasty differs between supine computed tomography and standing biplanar x-ray imaging: a pilot study

2025· article· en· W4411306757 on OpenAlexaff
Florent Moissenet, Sandrine Bousigues, Sana Boudabbous, Laurent Gajny, Nicola Hagemeister, Nicolas Holzer

Bibliographic record

VenueJSES Reviews Reports and Techniques · 2025
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsÉcole de Technologie SupérieureCentre Hospitalier de l’Université de Montréal
FundersH2020 European Research Council
KeywordsSupine positionMedicineArthroplastyMotion (physics)X-rayOrthodonticsNuclear medicinePhysicsRadiologySurgeryOpticsClassical mechanics

Abstract

fetched live from OpenAlex

Preoperative planning software is widely used to assist surgeons in selecting and positioning implant components for reverse total shoulder arthroplasty (rTSA)14. These tools rely on patient-specific bone surface models derived from preoperative supine computed tomography (CT) scans and predict postoperative impingement-free motion amplitudes by simulating uniplanar humeral movements. However, a major limitation is that the CT scans are acquired in the supine position, which fails to account for the scapula posture in a standing position, a factor critical for clinical assessments11.

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)0.001
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 score0.048
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.331
Teacher spread0.298 · 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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