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Record W62686587

Computational modeling of a prosthetic shoulder: our experience with the anybody modeling system

2007· article· en· W62686587 on OpenAlexaff
Yoann Collet, Patrice Tétreault, John Rasmussen, Natalia Nuño, Nicola Hagemeister

Bibliographic record

VenueEspace ÉTS (ETS) · 2007
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsDeltoid curveRotator cuffArthroplastyComputer scienceProsthesisShoulder jointShoulder ProsthesisBiomechanicsSimulationOrthodonticsPhysical medicine and rehabilitationMedicineSurgeryAnatomyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

When wear or tear of the rotator cuff becomes non-treatable, the glenohumeral joint degeneration can lead to upper limb pseudoparalysis, hence requiring a shoulder arthroplasty. Data indicating the performance of today's prostheses remain unsatisfactory and unpredictable when studying the case of massive rotator cuff tear (MRCT). Computational modeling represents a promising tool for exploring new prosthesis design possibilities. Nevertheless, no MRCT-simulation with a musculoskeletal software is available in the literature. The AnyBody Modeling System has been used to reproduce the MRCT-shoulder in vitro and then to propose a novel prosthetic design that would facilitate the patient's abduction movements following a MRCT. Results showed that with a lateral addition of 19mm on the humeral head, a deltoid strength lowering event occurred after 10° of abduction, reaching a level of -19% of the initial strength at 59°. This study has allowed for the generation of a new computational model that simulates a MRCT-shoulder, as well as the proposal of a non-anatomical prosthesis design which purpose is to reduce the required middle deltoid strength during 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.318
Teacher spread0.288 · 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 designSimulation or modeling
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

Citations2
Published2007
Admission routes1
Has abstractyes

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