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Record W2029531487 · doi:10.2147/oajsm.s62885

Transtendon rotator-cuff repair of partial-thickness articular surface tears can lead to medial rotator-cuff failure

2014· article· en· W2029531487 on OpenAlexafffund
Atiba A. Nelson, Ian K.Y. Lo, Richard S. Boorman, Michael J. Carroll, Randa Berdusco, Tom Woods, Stephen Sohmer, Kristie D. More

Bibliographic record

VenueOpen Access Journal of Sports Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of British ColumbiaSt. Joseph's HospitalUniversity of Calgary
FundersCanadian Orthopaedic Foundation
KeywordsRotator cuffTearsRotator cuff injuryMedicineLead (geology)Articular surfaceSurgeryGeology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to evaluate clinical and anatomic outcomes of patients following transtendon rotator-cuff repair of partial articular supraspinatus tendon avulsion (PASTA) lesions. PATIENTS AND METHODS: Patients in the senior author's practice who had isolated PASTA lesions treated by transtendon rotator-cuff repair were included (n=8) and retrospectively reviewed. All patients were evaluated preoperatively and at a mean of 21.2 months (±9.7 months) postoperatively using standardized clinical evaluation (physical exam, American Shoulder and Elbow Surgeons, and Simple Shoulder Test). All patients underwent postoperative imaging with a magnetic resonance imaging arthrogram. RESULTS: There was a significant improvement in American Shoulder and Elbow Surgeons (42.7±17.5 to 86.9±25.2) and Simple Shoulder Test (4.6±3.2 to 10.1±3.8) scores from pre- to postoperative, respectively. Postoperative imaging demonstrated full-thickness medial cuff tearing in seven patients, and one patient with a persistent partial articular surface defect. CONCLUSION: Transtendon repair of PASTA lesions may lead to improvements in clinical outcome. However, postoperative imaging demonstrated a high incidence of full-thickness rotator-cuff defects following repair.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.360
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 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

Citations22
Published2014
Admission routes2
Has abstractyes

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