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Record W4312066031 · doi:10.1055/s-0042-1756167

Rotator Cuff Injury and Repair

2022· article· en· W4312066031 on OpenAlexaff
Laura A. Fitzpatrick, Angela Atinga, Lawrence A. White, Patrick Henry, Linda Probyn

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2022
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of TorontoUniversity Health NetworkSunnybrook HospitalHealth Sciences CentreSinai Health SystemSunnybrook Health Science CentreWomen's College Hospital
Fundersnot available
KeywordsMedicineRotator cuffMagnetic resonance imagingTendonRotator cuff injuryTendinopathyRadiologyAtrophySurgeryUltrasonographyPathology

Abstract

fetched live from OpenAlex

Rotator cuff pathology is a commonly encountered clinical and radiologic entity that can manifest as tendinopathy or tearing. Magnetic resonance imaging (MRI) and ultrasonography offer similar sensitivity and specificity for the evaluation of the native rotator cuff, and the chosen modality may vary, depending on local practice and accessibility. MR arthrography is frequently used in the postoperative setting as a problem-solving tool. Key findings to include in the preoperative MRI report include the size and location of the tear, thickness of the tendon involved (partial versus full thickness), and overall tendon quality. The report should also address features associated with poor surgical outcomes, such as fatty atrophy, a decreased acromiohumeral interval, and evidence of rotator cuff arthropathy. Musculoskeletal radiologists should be familiar with the various surgical techniques and expected postoperative imaging appearance of rotator cuff repairs. Imaging also plays a role in identifying recurrent tearing, graft failure, hardware loosening, infection, and other complications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.259
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.009
GPT teacher head0.301
Teacher spread0.291 · 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.

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

Citations36
Published2022
Admission routes1
Has abstractyes

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