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Record W2523602798 · doi:10.1136/jisakos-2016-000076

Healing rate and clinical outcomes of xenografts appear to be inferior when compared to other graft material in rotator cuff repair: a meta-analysis

2016· article· en· W2523602798 on OpenAlexaff
Yohei Ono, Diego Alejandro Dávalos Herrera, Jarret M. Woodmass, Richard S. Boorman, Gail M. Thornton, Ian K.Y. Lo

Bibliographic record

VenueJournal of ISAKOS Joint Disorders & Orthopaedic Sports Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryAlberta Bone and Joint Health Institute
Fundersnot available
KeywordsMedicineRotator cuffSurgeryElbowTearsStatistical significanceTendonInternal medicine

Abstract

fetched live from OpenAlex

ImportanceThe use of grafts is a viable option for larger rotator cuff tears. Graft types can be divided into autograft (AU), allograft (AL), xenograft (XE) and synthetic (SY) material. However, the outcomes of each graft type are diverse.ObjectiveThe objective of the study was to compare the healing rate and clinical outcomes of different graft materials.Evidence reviewA systematic literature review was performed, and clinical studies of rotator cuff repair using grafts as augmentation or bridging were included. The primary outcome was tendon healing. The secondary outcomes included Visual Analogue Scale, American Shoulder and Elbow Surgeons Score and University of California at Los Angeles (UCLA) Score and forward elevation range. The studies were divided into AU, AL, XE and SY groups and compared. Analysis was a random effects model for healing rates and fixed effects models weighted by sample size for all the other measures.Findings23 studies with 25 study groups were included. A total of 130 (AU), 205 (AL), 111 (XE) and 202 (SY) repairs (mean age 63.7, 57.4, 63.5 and 63.7 years; mean follow-up 25.3 27.4, 28.6 and 26.4 months, respectively) were analysed. The estimated healing rates were 62.9% (AU), 78.9% (AL), 46.8% (XE) and 77.2% (SY), respectively. While no statistical significance was detected, the healing rate of XE was lower by more than 30% than AL and SY. The improvement of UCLA in XE was significantly less than the other 3 groups.Conclusions and relevanceThe healing rate and clinical outcomes of XE appear to be inferior, however, the results varied. While graft type affects clinical outcomes, other factors may also be as important.Level of evidenceIV. The use of grafts is a viable option for larger rotator cuff tears. Graft types can be divided into autograft (AU), allograft (AL), xenograft (XE) and synthetic (SY) material. However, the outcomes of each graft type are diverse. The objective of the study was to compare the healing rate and clinical outcomes of different graft materials. A systematic literature review was performed, and clinical studies of rotator cuff repair using grafts as augmentation or bridging were included. The primary outcome was tendon healing. The secondary outcomes included Visual Analogue Scale, American Shoulder and Elbow Surgeons Score and University of California at Los Angeles (UCLA) Score and forward elevation range. The studies were divided into AU, AL, XE and SY groups and compared. Analysis was a random effects model for healing rates and fixed effects models weighted by sample size for all the other measures. 23 studies with 25 study groups were included. A total of 130 (AU), 205 (AL), 111 (XE) and 202 (SY) repairs (mean age 63.7, 57.4, 63.5 and 63.7 years; mean follow-up 25.3 27.4, 28.6 and 26.4 months, respectively) were analysed. The estimated healing rates were 62.9% (AU), 78.9% (AL), 46.8% (XE) and 77.2% (SY), respectively. While no statistical significance was detected, the healing rate of XE was lower by more than 30% than AL and SY. The improvement of UCLA in XE was significantly less than the other 3 groups. The healing rate and clinical outcomes of XE appear to be inferior, however, the results varied. While graft type affects clinical outcomes, other factors may also be as important.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.363
Teacher spread0.297 · 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.

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

Citations12
Published2016
Admission routes1
Has abstractyes

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