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Record W2980221088 · doi:10.2106/jbjs.18.01473

Surgical Versus Nonsurgical Management of Rotator Cuff Tears

2019· article· en· W2980221088 on OpenAlexaboutno aff
Austin J. Ramme, Christopher Robbins, Karan A. Patel, James E. Carpenter, Asheesh Bedi, Joel Gagnier, Bruce S. Miller

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsRotator cuffMedicineTearsSurgeryElbowVisual analogue scaleRotator cuff injuryMagnetic resonance imagingProspective cohort studyPropensity score matchingCohortPhysical therapyRadiologyInternal medicine

Abstract

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BACKGROUND: Rotator cuff disease is a major medical and economic burden due to a growing aging population, but management of rotator cuff tears remains controversial. We hypothesized that there is no difference in outcomes between patients who undergo rotator cuff repair and matched patients treated nonoperatively. METHODS: After institutional review board approval, a prospective cohort of patients over 18 years of age who had a full-thickness rotator cuff tear seen on magnetic resonance imaging (MRI) were retrospectively evaluated. After clinical evaluation, each patient elected to undergo either rotator cuff repair or nonsurgical treatment. Demographic information was collected at enrollment, and self-reported outcome measures (the Normalized Western Ontario Rotator Cuff Index [WORCnorm], American Shoulder and Elbow Surgeons score [ASES], Single Assessment Numerical Evaluation [SANE], and pain score on a visual analog scale [VAS]) were collected at baseline and at 6, 12, and >24 months. The Functional Comorbidity Index (FCI) was used to assess health status at enrollment. The size and degree of atrophy of the rotator cuff tear were classified on MRI. Propensity score analysis was used to create rotator cuff repair and nonsurgical groups matched by age, sex, symptom duration, FCI, tear size, injury mechanism, and atrophy. The Student t test, chi-square test, and regression analysis were used to compare the treatment groups. RESULTS: One hundred and seven patients in each group were available for analysis after propensity score matching. There were no differences between the groups with regard to demographics or rotator cuff tear characteristics. For all outcome measures at the time of final follow-up, the rotator cuff repair group had significantly better outcomes than the nonsurgical treatment group (p < 0.001). At the time of final follow-up, the mean outcome scores (and 95% confidence interval) for the surgical repair and nonsurgical treatment groups were, respectively, 81.4 (76.9, 85.9) and 68.8 (63.7, 74.0) for the WORCnorm, 86.1 (82.4, 90.3) and 76.2 (72.4, 80.9) for the ASES, 77.5 (70.6, 82.5) and 66.9 (61.0, 72.2) for the SANE, and 14.4 (10.2, 20.2) and 27.8 (22.5, 33.5) for the pain VAS. In the longitudinal regression analysis, better outcomes were independently associated with younger age, shorter symptom duration, and rotator cuff repair. CONCLUSIONS: Patients with a full-thickness rotator cuff tear reported improvement in pain and functional outcome scores with nonoperative treatment or surgical repair. However, patients who were offered and chose rotator cuff repair reported greater improvement in outcome scores and reduced pain compared with those who chose nonoperative treatment. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

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)0.003
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0020.000

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 head0.035
GPT teacher head0.295
Teacher spread0.259 · 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

Citations45
Published2019
Admission routes1
Has abstractyes

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