MétaCan
Menu
← Back to cohort
Record W2054441953 · doi:10.1115/sbc2013-14037

Correlation Between Age and Tendon Strength in Supraspinatus Tendon With Full-Thickness Tears

2013· article· en· W2054441953 on OpenAlexfundno aff
R. Matthew Miller, Daisuke Araki, Yoshimasa Fujimaki, Volker Musahl, Richard E. Debski

Bibliographic record

VenueVolume 1B: Extremity; Fluid Mechanics; Gait; Growth, Remodeling, and Repair; Heart Valves; Injury Biomechanics; Mechanotransduction and Sub-Cellular Biophysics; MultiScale Biotransport; Muscle, Tendon and Ligament; Musculoskeletal Devices; Multiscale Mechanics; Thermal Medicine; Ocular Biomechanics; Pediatric Hemodynamics; Pericellular Phenomena; Tissue Mechanics; Biotransport Design and Devices; Spine; Stent Device Hemodynamics; Vascular Solid Mechanics; Student Paper and Design Competitions · 2013
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
FundersFondation ARC pour la Recherche sur le CancerAGE-WELLAchievement Rewards for College Scientists Foundation
KeywordsRotator cuffTearsMedicineAsymptomaticTendonIncidence (geometry)CuffRotator cuff injuryDegeneration (medical)Supraspinatus musclePopulationSurgeryPathology

Abstract

fetched live from OpenAlex

Degenerative tears of the rotator cuff tendons are a significant clinical problem, with an incidence of more than 30% in asymptomatic persons over the age of 60 [1]. As the population continues to age, the incidence of partial and full-thickness rotator cuff tears continues to increase [2], with previous studies finding that greater age also correlates with worse surgical outcomes and increased fatty degeneration in the tissue [3]. Although the relationship between age, rotator cuff injury, and poor treatment outcomes has been previously established, insufficient information exists on the biological and mechanical changes in the tissue due to aging.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueVolume 1B: Extremity; Fluid Mechanics; Gait; Growth, Remodeling, and Repair; Heart Valves; Injury Biomechanics; Mechanotransduction and Sub-Cellular Biophysics; MultiScale Biotransport; Muscle, Tendon and Ligament; Musculoskeletal Devices; Multiscale Mechanics; Thermal Medicine; Ocular Biomechanics; Pediatric Hemodynamics; Pericellular Phenomena; Tissue Mechanics; Biotransport Design and Devices; Spine; Stent Device Hemodynamics; Vascular Solid Mechanics; Student Paper and Design Competitions→Same topicShoulder Injury and Treatment→French-language works237,207→