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Record W4396978982 · doi:10.1016/j.jse.2024.03.055

Do therapeutic exercises impact supraspinatus tendon thickness? Secondary analyses of the combined dataset from two randomized controlled trials in patients with rotator cuff-related shoulder pain

2024· article· en· W4396978982 on OpenAlexaff
Marc-Olivier Dubé, Kim Gordon Ingwersen, Jean‐Sébastien Roy, François Desmeules, Jeremy Lewis, Birgit Juul‐Kristensen, Jette Wessel Vobbe, Steen Lund Jensen, Karen McCreesh

Bibliographic record

VenueJournal of Shoulder and Elbow Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversité LavalUniversité de MontréalHôpital Maisonneuve-RosemontCentre for Interdisciplinary Research in Rehabilitation
FundersAalborg UniversitetshospitalAalborg Universitet
KeywordsMedicineRotator cuffRandomized controlled trialSupraspinatus musclePhysical therapyRotator cuff injuryTendonPhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The mechanistic response of rotator cuff tendons to exercises within the context of rotator cuff-related shoulder pain (RCRSP) remains a significant gap in current research. A greater understanding of this response can shed light on why individuals exhibit varying responses to exercise interventions. It can also provide information on the influence of certain types of exercise on tendons. The primary aim of this article is to explore if changes in supraspinatus tendon thickness (SSTT) ratio differ between exercise interventions (high load vs. low load). The secondary aims are to explore if changes in SSTT ratio differ between ultrasonographic tendinopathy subgroups (reactive vs. degenerative) and if there are associations between tendinopathy subgroups, changes in tendon thickness ratio, and clinical outcomes (disability). METHODS: This study comprises secondary analyses of the combined dataset from two randomized controlled trials that compared high and low-load exercises in patients with RCRSP. In those trials, different exercise interventions were compared: 1) progressive high-load strengthening exercises and 2) low-load strengthening with or without motor control exercises. In 1 trial, there was also a third group that was not allocated to exercises (education only). Ultrasound-assessed SSTT ratio, derived from comparing symptomatic and asymptomatic sides, served as the primary measure in categorizing participants into tendinopathy subgroups (reactive, normal and degenerative) at baseline. RESULTS: Data from 159 participants were analyzed. Two-way repeated measures ANOVAs revealed significant Group (P < .001) and Group × Time interaction (P < .001) effects for the SSTT ratio in different tendinopathy subgroups, but no Time effect (P = .63). Following the interventions, SSTT ratio increased in the "Degenerative" subgroup (0.14 [95% confidence interval {CI}: 0.09-0.19]), decreased in the "Reactive" subgroup (-0.11 [95% CI: -0.16 to -0.06]), and remained unchanged in the "Normal" subgroup (-0.01 [95% CI: -0.04 to 0.02]). There was no Time (P = .21), Group (P = .61), or Group × Time interaction (P = .66) effect for the SSTT ratio based on intervention allocation. Results of the linear regression did not highlight any significant association between the tendinopathy subgroup (P = .25) or change in SSTT ratio (P = .40) and change in disability score. CONCLUSION: Findings from this study suggest that, over time, SSTT in individuals with RCRSP tends to normalize, compared to the contralateral side, regardless of the exercise intervention. Different subgroups of symptomatic tendons behave differently, emphasizing the need to potentially consider tendinopathy subtypes in RCRSP research. Future adequately powered studies should investigate how those different tendinopathy subgroups may predict long-term clinical outcomes.

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)0.042
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.094
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.019
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.041
GPT teacher head0.366
Teacher spread0.325 · 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 designMeta-analysis
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

Citations6
Published2024
Admission routes1
Has abstractyes

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