MétaCan
Menu
Back to cohort
Record W4377293038 · doi:10.3390/diagnostics13101819

Image-Based Numerical Analysis for Isolated Type II SLAP Lesions in Shoulder Abduction and External Rotation

2023· article· en· W4377293038 on OpenAlexaff
Javier A. Maldonado, Duvert A. Puentes, Iván Darío Quintero, Octavio Andrés González‐Estrada, Diego F. Villegas

Bibliographic record

VenueDiagnostics · 2023
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Waterloo
FundersUniversidad Industrial de Santander
KeywordsLesionJoint capsuleBankart lesionShoulder jointJoint (building)AnatomyExternal rotationAnterior shoulderMedicineCapsuleStrain (injury)SurgeryBiologyStructural engineering

Abstract

fetched live from OpenAlex

The glenohumeral joint (GHJ) is one of the most critical structures in the shoulder complex. Lesions of the superior labral anterior to posterior (SLAP) cause instability at the joint. Isolated Type II of this lesion is the most common, and its treatment is still under debate. Therefore, this study aimed to determine the biomechanical behavior of soft tissues on the anterior bands of the glenohumeral joint with an Isolated Type II SLAP lesion. Segmentation tools were used to build a 3D model of the shoulder joint from CT-scan and MRI images. The healthy model was studied using finite element analysis. Validation was conducted with a numerical model using ANOVA, and no significant differences were shown (p = 0.47). Then, an Isolated Type II SLAP lesion was produced in the model, and the joint was subjected to 30 degrees of external rotation. A comparison was made for maximum principal strains in the healthy and the injured models. Results revealed that the strain distribution of the anterior bands of the synovial capsule is similar between a healthy and an injured shoulder (p = 0.17). These results demonstrated that GHJ does not significantly deform for an Isolated Type II SLAP lesion subjected to 30-degree external rotation in abduction.

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)0.000
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 score0.161
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.039
GPT teacher head0.354
Teacher spread0.315 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDiagnosticsSame topicShoulder Injury and TreatmentFrench-language works237,207