MétaCan
Menu
Back to cohort
Record W2073473934 · doi:10.2147/oajsm.s7980

Rotator cuff troublemakers: pitfalls of MRI and ultrasound

2009· article· en· W2073473934 on OpenAlexaff
Bruce Forster, Chingkoe, Mark White, Louis Louis, Andrews

Bibliographic record

VenueOpen Access Journal of Sports Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRotator cuffMedicineUltrasoundMagnetic resonance imagingRotator cuff injuryRadiologyMedical physics

Abstract

fetched live from OpenAlex

Rotator cuff pathology is routinely evaluated in many imaging centers with both magnetic resonance imaging (MRI) and ultrasound. Despite good diagnostic accuracy using each of these modalities, certain limitations persist. In this pictorial essay, we describe five potential "troublemakers" of rotator cuff pathology which are recurrent themes in our busy shoulder referral center. The comparison of imaging findings on MRI and ultrasound are discussed. An awareness of these potential pitfalls will help improve radiologists' diagnostic accuracy of rotator cuff pathology, and allow the clinician to optimize imaging referral and better interpret the subsequent report.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.313
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.407
Teacher spread0.360 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueOpen Access Journal of Sports MedicineSame topicShoulder Injury and TreatmentFrench-language works237,207